{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's, but tracts for CA\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #from IL 5/31/24.  Using TRACTS for CA\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's, but tracts for CA\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - DO WE NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ??  #\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections count only if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, borderUnits, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     new for MN 4/7/24 - don't enforce contiguity here -- too slow -- fix in cleanup stage instead\n",
    "     \"\"\"\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        tryList = getAdjoiners(addedList,unitNbrs)\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP :  #and wontEnclave(UU, addedList, unitNbrs, borderUnits) :  #we'll post-fix contig'y\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:  #stop looping if no new neighbors found on previous loop\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def avoidedEnclave(UNITLIST, UNITNBRS, ADDLIST,MAXLOOPS=15):  #20 is arbitrary, but does suppress slender chains\n",
    "    \"\"\"\n",
    "    This method (new for WI 5-23-24) is designed as a shorter check than enclaveCheck for legitimacy of adding ADDLIST to UNITLIST\n",
    "    It tries to find contiguity among all ADDLIST's neighbors that won't be in the UNITLIST after the add.\n",
    "    It does this in successive loops, growing from one non-UNITLIST neighbor in loops to all its non-UL neighbors\n",
    "    If success before MAXLOOPS, it returns True (i.e. this list addition avoided forming an enclave), else it returns False\n",
    "        Note that this method will not usually allow a UNITLIST that doesn't currently touch map border to start touching it\n",
    "        (too many loops to go all the way around the UNITLIST to find the complement's connection)\n",
    "    \"\"\"   \n",
    "    willBeInUnitSet = set(UNITLIST+ADDLIST)\n",
    "    nbrsOfADDLIST = set()\n",
    "    for U in ADDLIST:\n",
    "        nbrsOfADDLIST = nbrsOfADDLIST.union(set(UNITNBRS[U]))  #these are the neighbors of the units we plan to add to UNITLIST\n",
    "    nbrsOfADDLIST = nbrsOfADDLIST.difference(willBeInUnitSet)\n",
    "    if len(nbrsOfADDLIST) == 0:\n",
    "        raise Exception(\"ERROR! Attempted to add list\",ADDLIST,\"but it should have been flagged previously as an enclave\")\n",
    "    firstNo = next(iter(nbrsOfADDLIST))  #pick one of these non-UL neighbors as a starter\n",
    "    firstGroup = getContigFromStarter(firstNo, nbrsOfADDLIST, UNITNBRS) #first find all neighbors that directly connect\n",
    "    foundNbrSet, foundTotalSet, newFoundSet = set(firstGroup),set(firstGroup),set(firstGroup)\n",
    "    LOOPNO = 0\n",
    "    stillConnect = False\n",
    "    if len(foundNbrSet) == len(nbrsOfADDLIST):  #ALL non-UNITLIST neighbors still DIRECTLY connect after the add\n",
    "        stillConnect = True\n",
    "    while len(newFoundSet) > 0 and LOOPNO <= MAXLOOPS and not stillConnect:\n",
    "        LOOPNO +=1\n",
    "        lastFoundSet = newFoundSet\n",
    "        newFoundSet = set()\n",
    "        for U in lastFoundSet:\n",
    "            newFoundSet = newFoundSet.union(set(UNITNBRS[U]))\n",
    "        newFoundSet = newFoundSet.difference(willBeInUnitSet)\n",
    "        \n",
    "        foundTotalSet = foundTotalSet.union(newFoundSet)\n",
    "        foundNbrSet = foundTotalSet.intersection(nbrsOfADDLIST)  #running list of complement-connectable non-UL neighbors of ADDLIST\n",
    "        if len(foundNbrSet) == len(nbrsOfADDLIST):  #ALL non-UNITLIST neighbors still connect after the add\n",
    "            stillConnect = True\n",
    "    return stillConnect\n",
    "              \n",
    "\n",
    "def avoidedIsland(UNITLIST, UNITNBRS, SHEDLIST,MAXLOOPS=15):\n",
    "    \"\"\"\n",
    "    analogous to avoidedEnclave but for shedding a list from UNITLIST; we look to connect in-UNITLIST neighbors after the proposed shed\n",
    "    \"\"\"\n",
    "    shrunkSet = set(UNITLIST).difference(set(SHEDLIST))  #shorthand for the proposed unitlist after shedding\n",
    "    nbrsOfSHEDLIST = set()\n",
    "    for U in SHEDLIST:\n",
    "        nbrsOfSHEDLIST = nbrsOfSHEDLIST.union(set(UNITNBRS[U]))\n",
    "    nbrsOfSHEDLIST = nbrsOfSHEDLIST.intersection(shrunkSet)\n",
    "    if len(nbrsOfSHEDLIST) == 0:\n",
    "        raise Exception(\"ERROR! Attempted to shed list\",SHEDLIST,\"but it was never connected to the rest of the unit list\")\n",
    "    firstNo = next(iter(nbrsOfSHEDLIST))  #pick one of these still-in-UL neighbors as a starter   \n",
    "    firstGroup = getContigFromStarter(firstNo, nbrsOfSHEDLIST, UNITNBRS) #first find all neighbors that directly connect\n",
    "    foundNbrSet, foundTotalSet, newFoundSet = set(firstGroup),set(firstGroup),set(firstGroup)\n",
    "    LOOPNO = 0\n",
    "    stillConnects = False\n",
    "    if len(foundNbrSet) == len(nbrsOfSHEDLIST):  #ALL in-UNITLIST neighbors of the shedlist still DIRECTLY connect after the shed\n",
    "        stillConnects = True\n",
    "    while len(newFoundSet) > 0 and LOOPNO <= MAXLOOPS and not stillConnects:\n",
    "        LOOPNO +=1\n",
    "        lastFoundSet = newFoundSet\n",
    "        newFoundSet = set()\n",
    "        for U in lastFoundSet:\n",
    "            newFoundSet = newFoundSet.union(set(UNITNBRS[U]))\n",
    "        newFoundSet = newFoundSet.intersection(shrunkSet)\n",
    "        \n",
    "        foundTotalSet = foundTotalSet.union(newFoundSet)\n",
    "        foundNbrSet = foundTotalSet.intersection(nbrsOfSHEDLIST)\n",
    "        if len(foundNbrSet) == len(nbrsOfSHEDLIST):  #all in-UNITLIST neighbors still connect after the shed\n",
    "            stillConnects = True\n",
    "    return stillConnects\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b68d8985-a7b0-4578-8b7f-7acdccf9e3bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  CA\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use ca_pl2020_vtd.dbf as this state's population data file.  ENTER 0 FOR CALIFORNIA\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  0\n",
      "OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf, but ca_pl2020_t.dbf for CA ca_pl2020_t.dbf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. CA has 39538223 peeps and is divided into 9129 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file.  ENTER 0 FOR CALIFORNIA\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf, but ca_pl2020_t.dbf for CA\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 52 52\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 58 counties in CA .  I will assign them county Nos 0 - 57\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 20 unpopulated coastal tracts.\n",
      "Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.\")\n",
    "if len(skipList) > 0:\n",
    "    print(\"Lets plot them and their parent counties\")\n",
    "    for t in skipList:\n",
    "        plotPoly(tractGeom[t])\n",
    "        plotCenter(t,tractGeom[t],6)\n",
    "    plotPoly(origMAP,0.2)\n",
    "    for c in range(nCounties):\n",
    "        if isAlteredCounty[c]:\n",
    "            plotPoly(countyGeom[c])\n",
    "    plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21fa0a5d-cf05-48b3-91f8-7845884f2a19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5394\n",
      "3007\n"
     ]
    }
   ],
   "source": [
    "for t in skipList:\n",
    "    if tractCP[t].x > -121 and tractCP[t].y > 38:\n",
    "        print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 20 units will be axed\n",
      "here is the proposed skipList [6643, 5394, 1451, 2435, 2441, 2502, 7618, 6467, 8888, 7610, 3007, 7316, 1212, 2346, 5135, 7126, 60, 8937, 2380, 7624]\n",
      "here is the final skipList [7618, 2435, 6467, 2502, 7624, 2441, 2380, 5135, 1212, 7316, 7126, 8937, 2346, 1451, 6643, 8888, 7610, 60]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "keepSet = {5394, 3007}\n",
    "if STATE == \"CA\":\n",
    "    skipList = list(set(skipList).difference(keepSet))\n",
    "#skipList = [146, 3084] #...  #alter here if needed - nothing for MO\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 7\n",
      "removing coastal units from countyNo 8\n",
      "removing coastal units from countyNo 11\n",
      "removing coastal units from countyNo 18\n",
      "removing coastal units from countyNo 20\n",
      "removing coastal units from countyNo 22\n",
      "removing coastal units from countyNo 26\n",
      "removing coastal units from countyNo 29\n",
      "removing coastal units from countyNo 30\n",
      "removing coastal units from countyNo 36\n",
      "removing coastal units from countyNo 37\n",
      "removing coastal units from countyNo 39\n",
      "removing coastal units from countyNo 40\n",
      "removing coastal units from countyNo 41\n",
      "removing coastal units from countyNo 43\n",
      "removing coastal units from countyNo 48\n",
      "removing coastal units from countyNo 55\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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3bnSlX+teYq7FMS/kX5qdiyPd0or3E2OYmpFAiK8rL/bvy8YOjY3HOttYPcaalp8kiZYQQRCEqkC0hNxPgQYit0Grdx/J5dRBLTB3hdy4XPJP7sAkuNMjuW4hWZb5Y9MO3jd3AjtD99ObN6/x/svPFTlu+9GTYGLPfE0yTTs82jo+NEkMTBUDUwVBqApEEHI/FzdBfiYEPf/ILmnVthW5Kw+Qf/bkIw1CZq/fygKFBemWTvjeTGB5iwZ4uDqhUDcsduyx5HSoZk+t6sUXYavypCe3O+aJD74EQahSRBByPyeWgqMfOPk9skuaNWgMKw+Q8P0ianToi8q9YldVvRh9md+OneaGVo+kkIhWmpCnVHHJ0YWeV6PoprSnY/smODs7lVpGZHIqVKuB6f/Z+jfAEx2EFBINIYIgVAX/h0+QR+haKETvhL4LH+llrV4YjrNSyewDl0lauYnPXn4RE40GVy+PB179VK/X89yqLZyydSBfpcbOwh6PnEw0kgJvTQ5ZSiVDc24y9LU+KBT3Hyo02tmGV4D2ibmMmDeW7of3Yq3JLn5gKdWV7rP/frM3HuYhqi8AU1sd+sm3Z/VIcj5Z+pYkaccWP0Euz8VkpCo89URCQo2SzKxMHLB83NURBOEJJ8lVrH02IyMDW1tb0tPTsbGxeXwVubgFNowFE0t45zAolI/08h9vX8lvyqKtL54pN+ip1fBcwzoE+/uWqzxZlqm2+5Sh7IwEBndsjY31ww0o3b59NT/Gp3PQoxHVMxPYFnpXCvpS/mSV+EdO5nbgUdJ5cgk/Gs8py0P/jpT6+TpUtjaYe99eJVl96Xuy9XZcqbWotNMqVGnBZJn/Mpa3XrcKzs/P4+LlKFq90Q0vb+9yFiIIglC6B3l+i5aQ0oS8DbkpMPjfRx6AAChlHQ4FaSyu5srKs1H421uzUqdlsZ0rP8Vl8fqFbUx9pkuZyzt13rD4XqNrMbz72nMVUsfOnfvSGXjn3z84onbCYcriCin3cYhZcAbrG6HUG/R/NNX4Ady4cYN/5u2lleiPEQShChBBSGlkHXi3ghqtHu1lZZnhW/5mrWkA7fMu0axee5rVqwPA60BWdg4vbDvEb9aO1Fy7iRfat0Sfk8v1rGzyFQqsTU1xd3LA0tysSLkr9h8D3yAm1/aoiEoSFnmUvbHRXMyHNeZBdMq58PDlPkYSUimLEv63FLbAVLEGUEEQnlAiCCmNNh80GY/8srIss9bUHwAnSYus1yPdMUbDytKCDc90YPjS1Xzi7ccnJy8XK8My4gpvZCfTp3kjansaptm+GBzIslQtz+SocF6/hx1N/HFxf7CZLSGhWxme6QqKQBpIsbypvcDbjRo9UFlVx5P1UBZBiCAIVYFIVlYa/+6QHAmrhsLVw4/ssgqFgqXOWdTPvcoqk9qsPL2/2DEqhYKfB/YlxFLHjPxUZko5rDDJ5y9lDjM1yTRISWK+tTMdz8exes8hABo3CWanjwsfpSdww9qWYbuPkq/VPVAdG1UzjCV4XRfFlu69+bzLS7hXe3Szh4QH96ADmwVBECqDaAkpzdOzwc4LDnxnaBF5ddUju3TXuq0JcE+g2akEZiXm06+EYySFghbNGtOihH2vAdnp6fTZuJeJltb89XsIkZY2WOfn0dfcEHceq+ZFys2buLm5llBC6XI12fx+LgyUATSxMbvv8fczY8YMJkyYwOjRo5kzZ06RfbIs07NnTzZv3kxISAjPPffcQ1/vXp6EtgHRHSMIQlUiWkJKY24PnSaDlSu41Hmkl87Pz2PG0b0AfOSgfaAyLG1t+apuLbxzMtGoVHTJy8JdV8B0G0MXzJAbseUKQLQ6HV127sH/wFnmKgN4Ku8K7eu2faC6FTp27Bg//fQT9evXL3H/nDlzHtknd+mJCEFuE0GIIAhVgQhC7uXKAchKhDq9jZv27t1L7969cXd3R5Ik1q5dW+SUNWvW0LVrVxwdHZEkibCwsBKLTi8wBBd7wrbx6Y7VnLlwCFlvWM9k8J6trDGtzThFDH2adn/g6jesH8S//Xuzvn9vvu7/LCsGPMtWFzP2etkxvV/v+xdwhw0ntxEu2RKsv8kWLz1ruz+LncWDT6HOyspiwIAB/PLLL9jb2xfbHxYWxjfffMNvv/32wNconzvnCP93ie4YQRCqEhGE3MvRn8G+Jng0NW7Kzs6mQYMGzJs3r8RTsrOzad26NV999VWpxebp9fjvj8BtVxgvpTrzq8KHTnHmdNuygQGb17BT5YlTfipvNKr4VXTrBwVQ26dGuc+LvhmHpTaHde070MDn4Qehjhgxgl69etG5c+di+3JycnjllVeYN28ebm6PJi28LINKzq/069wviJUkqcSvmTNnVmg9REuIIAhVgQhCShN3Es6th7bji6Tn7NGjB1OnTuX550teS+a1115j4sSJJT5cC5ncUd4biliOB1jyuel1TCQ4qHLnDfkS4e2bYW/tWOL5kydPLvaQCggIACAlJYVRo0bh7++Pubk5Xl5evPvuu6Snpz/Iu2CUnnGDbJUFaTkPP2NoxYoVnDhxgunTp5e4f+zYsbRs2ZJnn332oa9VVnpZxl6fQnrYP5V6nfsFsfHx8UW+fvvtNyRJom/fvhVyfTEmRBCEqkQMTC2JXgebPgKn2lD/pQovXpIkPlFeZpquJr/oPRmol3mzZS/eLEcZQUFBbN++3fhadWsNl7i4OOLi4pg1axaBgYFcuXKFt99+m7i4OFaterDBtbtObOZXp84EZF3CyjLogcooFBsby+jRo9m2bRtmZsUHtq5fv56dO3dy8uTJh7pOeVl2fA9WbCUn+Rq2lXidHj160KNHj1L3393ys27dOjp06ECtWrUq5PqiO0YQhKpEBCElOfgDxB6BIf+CsnLeouEtn2bTtk2cMPOizcUc4qsVzQdyPyqVqsSuirp167J69Wrjax8fH6ZNm8arr76KVqs1BitlteHIekZlOlM3N5qQpg1QqdTlOv9uoaGhJCUl0eiOvCI6nY69e/cyd+5chg8fTnR0NHZ2dkXO69u3L23atGH37t0Pdf3SWNgb3kt9FWocTExMZOPGjSxZsqTCyxYtIYIgVAUiCLlb1A7Y+QW0ehe8W1baZVQqNf/2eIbm/27kinl1Em5epZpLjTKfHxkZibu7O2ZmZrRo0YLp06fj5eVV4rGFefzLG4DIsszwrGoUKNX82a4jljalr6pbVp06dSI8PLzItiFDhhAQEMCHH36Ik5MTb731VpH99erVY/bs2fTuXb7BtOUh62/lTJEefYr+0ixZsgRra2v69OlTYWWK7hhBEKoSEYQUkmX4oRGkXAK/btBx4iO5bF1FLleAb86EM6uMQUjz5s1ZvHgx/v7+xMfHM2XKFNq0aUNERATW1tZFjr158yZffPEFb75Zns4eg4ysFAoUatrmx+Bo07Dc55fE2tqaunXrFtlmaWmJo6OjcXtJLTxeXl7UrFmzQupwL5JUdVpCfvvtNwYMGFBit9WDEt0xgiBUJSIIKXRqhSEAAXjht0rrhrnbJ0F12BhVwP7csj8c7hxTUL9+fZo3b463tzd///03w4YNM+7LyMigV69eBAYGMnny5DKXX5CvYcG+NczTeWItKRjj899fbdXYMlBFntH79u3jwoUL/PXXX5VSvmgJEQShKhBBCBjWiTmx1PDzmAgwLX2J+6ysLKKiooyvL1++TFhYGA4ODnh5eZGSksLVq1eJi4sD4MIFw8Jubm5uJX7Cr+UZhO/pdaSrSr/m/djZ2VG7du0i9crMzKR79+5YW1sTEhKCWl32sRz7Lx5jmiIQD10yGxoH4uZQ7YHrVhb3G+fxKB6Ysqyv9GuUx8KFC2ncuDENGjSo0HJFd4wgCFVJ1Wl7flxkGda9A9ePw6B/wM7znocfP36c4OBggoODARg3bhzBwcFMnGjovlm/fj3BwcH06tULgJdffpng4GAWLFhQapmNTfXUyE9+4FvIysoiOjqaatUMwUJGRgZdu3bFxMSE9evXl6s5/8D5Iyy8ch2AD+wLKj0AqSoKE8VJisodE5KVlUVYWJgxiV1hEHv16lXjMRkZGaxcuZLXX3+9wq8vghBBEKoS0RKy7xsIXwkvLoGa909D3r59+3v+Az548GAGDx5c7mrI5ZgZ8/7779O7d2+8vb2Ji4tj0qRJKJVK+vfvbwxAcnJyWLZsGRkZGWRkGHJ7ODs7o1Te+yE7MyaRwxYBDMw9xYvtXiv3ffy/elQDU48fP06HDh2Mr8eNGwfAoEGDWLx4MWDIoyLLMv3796/w64sxIYIgVCVPdhByYRPsnArtPoKg5x5bNSRALsdghGvXrtG/f3+Sk5NxdnamdevWHD58GGdnZ3bv3s2RI0cA8PX1LXLe5cuXqVGjxj3L/irQl3bR+axW+zE1LxsTc+t7Hv+fURiElCMYfBD3C2IB3nzzzQcaSFweoiVEEISq4MkNQrJvwtp3wL8HtPvwsValvJ9NV6xYUeq+sjzk7sXbrRZ+Z3YSaebOH6HbGNK64qaHVmm3xoRIVWiKbmUQ3TGCIFQlT+6YkE23Ao/e31f6p9+yqCqPBDMTM5YG+wOwLF0yjpX4r5PlwpYQEYQIgiA8Kk9mS8iFTRCxCp7/GaycH3dtqswy8jsj9vH79SQ2mfgAcMm0Glk56VhbFV/l9j/HOCbkvz1mQgQhgiBUJU9eEJJyGda/C75doH6/x10bo/KMCaloWp2Onw5t5IsCLxwkPQPlGF71C8TexuPJCEC4Y2Cq4r/9V0IMTBUEoSr5b/+LW5JDcyE/C577scp86n1ctUjPTKbzwRPEmjgDXtjpsjnSqiHWlpW5hFvVlJccC1StjKmVQbSECIJQlTxZQYgsw5WD4NEUrFwed22Myjs7piLM37+OL/I90Js406HgKm+5WhLs1xRrC5tHWo+qQpt6DQAzl4pZrbaqEkGIIAhVyZMVhBxZAEln4dXV9z/2kZKRH8EncL1ez/xDG/k1y5J4E2+aZZ9nSqA/wT7PVPq1y0OXm4zCxAZJacjyKssy+QlHiLv4A/maRGR9AVo5F7XCCjNTN3ILkrghx1Cg1GORr8ZFHYCkMsNU5UhqVhi5ZIBej15XgMu5bNyS1YaAFBlkGUnS42WZDcCFoaOQ5Vu/i8JjKPwmgwzSrQe4umcPgj98vDOryksEIYIgVCVPVhByw5BCHRuPx1uPu1R2G0jImaN8E5tCtIkLsuQJJjBVfZlh3Z5FMjGv5KuXTJZlNDdCuRmznPT47WSrskGhIstUazxG0oO1RkGmmQ5ZIaHUyZjpVCgkJUrJhEzSyNNeQSHLuOlrYG7qQVrBBa4SgVQgo5UlzCQF9npnUCmJN48js6k5+Yfz8MjzMnTHSRJIKtILICM1G52z2+1xE8bvxv+BJCFLYHLmLHmHDj/S96wiiCBEEISq5MkKQjpNhJh98GtnGLYFXIMed42MKqs75mpCNMOTTMDUjUmcw8/GjvYNu6BSNnyg8mS9juunv+BSwhK0Konqel/MTN2wtGtIQW4Clta1yVZpMDNzRymZokuLRpNxkYzci2gKkkHWctM8q2ih1mCq0WOrcMJGZ4652gWlrCJHTiLbLAMvEy/MzTxwrfchKvN7z2a6M+l+fsYl1FZeSLcGmzofC+F05vsog4dg0eOLYufa3nX+vRx+uncZj6xaRBAiCEJV8mQFIRYO8PoO+LUT7J4OLy173DUCbjfvVySttoBV+/9iep47mDjwr6eWRr6304DLBXlkX1yOTqlAk3GR3PRz5GqTiDOJxzJPSTXLp3BvOAW1bU1knY7UC7+SemU11xWRFKgVYKIAWeaGfIk8XTQkHwBgyfwUfl+aVqQunp5qlv/siTnmLNuRyM6dWURF5pGTIxN74neq1++PdJ908g/CxKbo+A7LmvXhNMTm/4V6sw3mbn7Y+bRGZeX4xMwaEUGIIAhVyZMVhACY24HnUxD+N+SmGV4/dhX/QPh45xqWquviJycQUseaGs4e6PVaZF0++uwEzmxrR7KjifF4JWCBCS666iSZXSdKd4DoY52w0ajJVRWQbyKBKXjkeeNs1x37uqONXTl6bS55icdR2HiwfeMkavtuYfVPb2Fq4gImVlh7dcDNzRuAfRfn0P+lXJAkJkyYgFXNpyslACmJuWMtHOLqkOJ+jkgWQAqQAsoMJYHuX+LS7IVKvX5eVhYp586hsrLCKSDgiQl8BEEQSvPkBSEAdZ+HsGWGrpk6VaVZvWIfSFtkQ7fFAuVP3DhxlhjTAuQ7HnqSvZog8+exrN4ZU1tf1DY+xodigeYG+SlnSI5ZQWrBcdSSEjtFbao/NQeVmWOxaylU5phXbwOAqXUtzC2rUbfj5yXWa8yYMQDs3r27Au+2bCRJIvjVDWgLsshLjSP7chjXLywhxeM84VkfUi8UXBpXfCCSk5PD9pdewi8yyrjtJpBla4tepUTS6rD/YDyurVphbm+P2tS0wutQqLAFRARAgiBUBU9mEBJ7FExtwafj464JYMiYKlfwM6Gb6hxLacUKpRNNZU8amfiAJJGXex0FKhx8BmBbq+QHrtrMGbV7eyzd2+P1ANeOjIzE3d0dMzMzWrRowfTp0/HyepCSKodKbYXKpTaWLrXJij9NCucBsK4eXPZCZBk0mvseFrlyJdnTvsTv1rFZ3boiqVSGdPj5BZgcOICZRoP2k0+5Xli/qV9Q8+mnUZmZlffWylBtEYQIglB1PJlBiCYd8tLh1ApoNAiU/723YXTjpzl2Yh+/SCP4RQ219Al8XNOBp72aVep1mzdvzuLFi/H39yc+Pp4pU6bQpk0bIiIisLaueivy1njmcy7v/hMAE4dqZT9RrcY2OprD3XuwtWYNVh04QExMDABBQUG8P3w4XosXYxUXz7q0NNbptERpNGR+/z2pqanY2dkZi8pMSOD61q2krQnB9vx5tJ9+RuSnn5Hu50udH3/E1rOsw2XvTwQhgiBUJf+9p29ZtJ8AGXGwcRycWw+9vgVHn8dWncpIVlbdzpsd7T2Jz7nJ7oQzvBfrxoRL6TxdyQ0SPXr0MP5cv359mjdvjre3N3///TfDhg2r3IuXk16vJ+HQYgBsUmqhNLEo87k+X04jqe8L2MbE4BQRzptqE9xq+6OztyO0Xj1eHjqU1Z5eeNjZoejVk37Ohu6xCRMmFCvL2s2NgIEDYeBAMq5dJ+rXXzD9629sI6OI69KV5G9m4d2tG0rVw/91FUGIIAhVyZMZhJjbwUu/Q9R2+GcMzG0KjQdBs7fAJeDR10eWK2WKrkKhoLqVCwN8Xfj+2r/kyY8+JbmdnR21a9cmKirq/gc/IvEHFnM27wukLJCtQJGpwLfuJ+UqwzkwENvQ46THxNBo5izQ67E9cgSysqgfe42fZJnTubk8c/kSjW6dU5ZxMDYe1Wk0eTLypEkc698f67BT5L33PsfmL+CpDf+U/2bvImbFCIJQlfy3F8q4H9/OMPIYdJkCZ0Lgx+aw7AVIi32k1ajsz6Q6vY4rsjsWkha9Xl/JVysqKyuL6OhoqlUrR1dHJbsS9SMAplESNS49Q9teEdgHtC93OSYWFjgHBvLUot94asli/E6Fkd6rJ6scHckFGj9ENlVJkmi2YgWu69cBYBsVxbmAOlzbv/+By7y7fEEQhMftyQ5CANTm0HIUvHcB+vwKN87Dwq4QvgpyUx9JFSTkSpike9vFtMsAXJI90FfqleD9999nz549xMTEcPDgQZ5//nmUSiX9+xtylCQkJBAWFmZsGQkPDycsLIyUlJRKrVehhMO/k+2ZDIDtXyr0v59EqXr42Sjh4eHYOTrS+rvv+OZMBGv/+YceY8c8dLkOtWvjtX8fGU0aA5D5+huENmlK2E8/kZucjCYlhZwbN8rcwiG6YwRBqEqezO6YkqhMof6LULMNrBoKq4eBpASvFtDgZQh+tcqsulte/na1aKBczyldDS6lX6O2vXelXevatWv079+f5ORknJ2dad26NYcPH8b51piIBQsWMGXKFOPxbdu2BWDRokUMHjy4QusSufZ9cnNikPUymoJ4srwTjfvU8WrUsi3axETON2pMwInQh7qWv78/YWFhpKens2rVKgYNGsSePXsIDAx82NvA0smJ5suWEblqFenffItlairMnkPM7DnGY/SShM2C+Xi0bXvPAKOwJUwEIYIgVAUP1RIyY8YMJEky5n4A0Gg0jBgxAkdHR6ysrOjbty+JiYmlF1LVWLvBkH9h7FnoNQvUZrB+JGyqvIXKDFN0K++hoFAoGFjNCoAtiZXb1bRixQri4uLIy8vj2rVrrFixAh+f24N+J0+ejCzLxb4qOgDJjj/PVZsQbridJM3iDBrbFJRpSuwSfGjmu5rW/c/iMWcOAHJODukbNjzU9UxMTPD19aVx48ZMnz6dBg0a8N1331XAndzm98ILNDl0kFpHjyC//x66sWPQjh1DWosWKGSZrLfeJvSpp9Dl55daRmFLiEIhGkEFQXj8Hrgl5NixY/z000/Ur1+/yPaxY8eyceNGVq5cia2tLSNHjqRPnz4cOHDgoSv7SNlWhyZDDV/7voUdU6DFO2Bf43HX7IFYm9oBsOFmOqMeb1UqnSblOofP9QKgjnoC7n1eL/G4rL17jD8rbG0rtA56vZ68vLwKLbOQqY0Nga/fcU9vvUXkqlVcW7Uat7Awci5fxtrfv8RzRXeMIAhVyQMFIVlZWQwYMIBffvmFqVOnGrenp6ezcOFCli9fTseOhkRgixYtok6dOhw+fJinnnqqYmr9qPl0MAQhf72K3HwEVG+MLCkM3TOyBJLC0JIhKYyvkbj1XQEUrtYq3fH6ju/6ypkdc6dnvJow78p6IgscKvU6VcGhA+3BEsxvOOL6bP8Sj9FcvEjK4iUAOA4fjnWbNg98vQkTJtCjRw+8vLzIzMxk+fLl7N69my1btgCGcTAJCQlFxsFYW1vj5eWFg0PF/D78XngBvYMD+ndGcH7kSII3bEBVQuZVEYQIglCVPFAQMmLECHr16kXnzp2LBCGhoaEUFBTQuXNn47aAgAC8vLw4dOhQiUFIXl5ekU+MGRkZD1KlylWtITQfDpd2se/344TnuFZo8eHBtbjpruDk1TSg6DTKzBQNKlMl5laGdV5kSTbmFSl8jsiS4aEiy7fiIm4NX5EMiT0LD7yicyYHcyJunKOuc50KvYfH7WboWtKvHSU7Oxq9mx51siktXzpa6vHJP/8CgEXzZriMfvehrp2UlMTAgQOJj4/H1taW+vXrs2XLFrp06QI8unEwtTt04JSZGVax1zjRtRvN9uwudowYEyIIQlVS7iBkxYoVnDhxgmPHjhXbl5CQgImJSZFskACurq4kJCSUWN706dOL/ANdJUkS9JgBgM+RU4QvMsyuCGoo4VrNMKbDQA9y4Wv5VgRQ+B2Q9UW/39p3IM2Um1Zm9IiOKb0OFTKsxrDg3PzwJczrOKMiCnzs9DodZ5b1J8k9FKwxfAFBdb6953mqW1OG1e7uD12HhQsX3nP/5MmTmTx58kNf534kSaLesaNcrFcf68REDnTqRLONG1Hfkf5dtIQIglCVlCsIiY2NZfTo0Wzbtg2zClrXYsKECYwbN874OiMjA88KTFNd0ao3b8CrtXLZ+msE0ZEaGjzXCHs3y4cqc35OAXsv3TS+lu7omklPzMHETImFrQmFsc2tphDDj4VxDoUtJIZtxoeNfCtEuvU69upf1LILIfxoQ+o1e/mh6l0VnA0ZQpJnKM43m1LnmfkgSygUapSm9/6dFFy9AoAuvQq2vD0EpVpN9e3buda5Mw7X44hqGIzV99/h2bUrIAamCoJQtZQrCAkNDSUpKYlGjRoZt+l0Ovbu3cvcuXPZsmUL+fn5pKWlFWkNSUxMxM3NrcQyTU1NMa3EVUMrg62zOb3fbcjqr0NZ/XUorV/0I6DFgyfjsrVQ07tuKefXfeBii7l8bi/mdvMAyM15NDlQKotcUEBm9DGSLA9gd9WH+oNXADB//nzmz59fZB2XiRMnFkknD5C5ZSsABXHX+a+x8ahO4PlzhH75JRZLf+fGlM+NQYjojhEEoSopVxDSqVMnwsPDi2wbMmQIAQEBfPjhh3h6eqJWq9mxYwd9+/YF4MKFC1y9epUWLVpUXK2rADNLNX3eb8Tu5RfY/ccFXGvaPHSLSGW7dmUXmICjyXQatu73uKtThCzLHP+jIxnuV1Elm9zeIRVPwqV1KDD+rNAp8G46xvjaw8ODGTNm4OfnhyzLLFmyhGeffZaTJ08SFBR0+zwbG/QZGegzs9BrNCgqYcXax63xxx9zbunvSHes9iu6YwRBqErK1SZrbW1N3bp1i3xZWlri6OhI3bp1sbW1ZdiwYYwbN45du3YRGhrKkCFDaNGixf/vzJh7MLc2ocuQQKwcTFn/XRgXjiSgzdc97mqVKle/Ab3W9KEDkL1799K7d2/c3d2RJIm1a9cW2S/LMhMnTqRatWqYm5vTuXNnIiMj71lmzD+TyXC/CoBtvj+2BQHYFgRglx94x1cQdvlBOMbXxSbBi5oMoVWb/TgF9TSW07t3b3r27Imfnx+1a9dm2rRpWFlZcfjw4SLX8925A4CC69e50DCYK4MGP9R7UlVpVSry71i9WAQhgiBUJRWeMXX27NkoFAr69u1LXl4e3bp148cff6zoy1QZKhMlz7zbkL1/XWT7orMcWW9G3w8aY2lb9bqYZL0CpSoPnVb7UCuyZmdn06BBA4YOHUqfPn2K7f/666/5/vvvWbJkCTVr1uSzzz6jW7dunD17ttSxRLFZf4MVtG93HqVS/cB1u5NOp2PlypVkZ2cXa4lTWlnhu3cvcR98QM7hw+QcOUJk27b4bNqEwvLxtGjNmDGDCRMmMHr0aObcSqTWvn179uzZU+S4t956iwULFpSpTEmWkdq3M74u7I5RKpUVUmdBEISH8dBByN0rg5qZmTFv3jzmzZv3sEX/37BxMufpEQ1Ivp7FyunHOX8onsbdazzuahVT03Mysckj2bKjEbl6C/QlNoTJJf54twYtQYuhNeFA6Efkm3yNjGGa8IyvjvF03+qkSzM5eRmeGSCz6dUYxk9sRMt2LtxKosLtpfskXNwMWT77fv0No+q1ptPTrR/4PsPDw2nRogUajQYrKytCQkJKTJ+udnHGe/Eick6Hc6VfP7RJN7jQuAmO7wzH5d2Hm7ZbXqUl/wN44403+Pzzz42vLSwsylSmLMsodDrkOwLOwiBEDEwVBKEqEGvHVCDH6lZ41LHnSkRylQxCfOp24tjsV7hmlYKLtwWSpOfuNXwNrxTGHCRS0f/dRQYOU6AMIF/lC8gkxaeSlnqQOk1aci7FDEsTJd6O5tTyj+HCRTVNO9ZFvjWbp3CKsozM9dzqSPmXSdI68fXOSzRtWQ8bhwfLYlredVws6tfDP/w0kS1boc/MJPnH+WTv34/3kiUozM3Lde18TTabNv/IiqtribTIYKyqO/0HfnXPh35pyf+M9bOwKHVg971c338ACTC1sjJuE0GIIAhViQhCKpidiwWRx6rmWjlKpQk3otpjhYTO1JHXhzR46DIHMpP2DQfzXNfnADh48CCwiKHPzaJatdszfv5ZGI8kSQzsPrfUsrQ6PVcv/MUilQX1v96PZYGGaY2teO6VbuWqU+E6LgCNGzfm2LFjfPfdd/z000+lnqNQq/E/dpSbP//MjW9nozkdzoXgRjiNHIHzyJFluq42T8NrCzpy1j4HH60aU1TMkDYzc/EmJtq8Qp8XPkaW5WLjMUpL/lfojz/+YNmyZbi5udG7d28+++yz+7aGXN60Cc1Yw9R3t1vJ0QAycjLIV+SLIEQQhCpBBCEVzN3XjlM7Ytm68AydBtZBqa5a/9jXe8WP88ujyDuSzKX2adSqafe4q2SkUir45KP+PB96np4ro8lWmzHmtJbnXnm4csuzjovTm29i98ILRLZsBcDNufPQpafj9skn9z332oUTnLXPYbSqK6+P+waAdetn8WnqEiZl/8mkJX/imCFTJ9+ZNu6tePGFz/jg46Fs3r6R1z/qzMQF/YiJO0uu4jqfzr8GgLnzTTr188PS1oyk6+nMXTCH1f8u5vk3mhrTxhT+Xyr8UYKcuHgUPRWkONijuDAHzhtan8IKwsAbBukH4Ub5W1cEQRAqUtV6Qv4H1GzoRIfXAog+kcSR9ZfKdM706dNp2rQp1tbWuLi48Nxzz3HhwgXj/pSUFEaNGoW/vz/m5uZ4eXnx7rvvkp6eXu76dWrrhcbXMPDy4P5r5T7/fgq7De5eOfleuWLu5uhizyS/269zsnLKfP0JEyawd+9eYmJiCA8PZ8KECezevZsBAwaUuQyVgwN1zp/DfbYh62rq78u4+FQL9Lp7z3x6Y5+hxcTL+/a4jmefeZ+dXdbSI6MG7WKt6awP4IYqhxl5a2k7owE//PwXNsOdOamP4nheJJn6HJJ0aZzKi+ZU3iVUrUzIrpnFTdcMlMES9V/xJPJ0AicTLhOljSNaG8elggQuFSQQrU0gShdPtDaB604yUTWtSaxuTrwmnoS8BJLyk4zjfJTmYmCqIAiPn2gJqWCSJBHYyp34qDSiQpNo8bwPkuLe0yH37NnDiBEjaNq0KVqtlo8//piuXbty9uxZLC0tiYuLIy4ujlmzZhEYGMiVK1d4++23iYuLY9WqVeWu47j3mvHNOzsx1Wgf9DZLVbNmTdzc3NixYwcNGzYEDFlwjxw5wvDhw8tUxiffb2C70hCwfNvABAursg3EhPuv41Ietj16oHZ15cqAV9GlpXEhqC7us2Zi+/TTxmN0Oh1HD6/n7+M/k2BXgHOaTNfWg4qU4+zuw9ej/imyLfTQGsbvmoAuQ8flyZe5fEd5qZdyiD2QSl5eXrFZLNnZ2Vj9aMV7bWbRrVv5uqkATiadZOCmgSgk8flDEITHTwQhlcTdz47zhxLIy9ViZnnvKaebN28u8nrx4sW4uLgQGhpK27ZtqVu3LqtXrzbu9/HxYdq0abz66qtotVpU5ZxuK0kSOgXk5j5YEJKVlWVcERbg8uXLhIWF4eDggJeXF2PGjGHq1Kn4+fkZp+i6u7vz3HPPlan8IHsV2zOgtuYmffoPuv8Jd7jfOi7lZdGoEf5nIrjS7yU0ERHEvT+emwt+wnPpEk5s3Mjm6GgKpAKOul7nfakbr46YXqZyG7fow7q6Xbgy/kqR7Xcm/ytpGm1YWBhAkfE25WFcHFGkCREEoQoQQUglKfy3Pjs9775ByN0Ku1nutcx7eno6NjY25Q5ACunMFKTGZpKWkmtYeVc2rMarl0CpUmBnVXqek+PHj9OhQwfj68K1fwYNGsTixYv54IMPyM7O5s033yQtLY3WrVuzefPmMq83dDJNBgWMaFH9ge6toikUCmquWknMgAGkRpwhpqCAkKnTyLIxJAHTKgvYPvgg5mZW9ympqMLkf3e6M/lfdHQ0y5cvp2fPnjg6OnL69GnGjh1L27ZtS5zKWxaycfyIiEIEQXj8RBBSSZw9DQ+oqxEpOLqX/eGk1+sZM2YMrVq1KvaAKnTz5k2++OIL3nzzzQeun5uswCZXT9bXx4vtK0Amtp8f9RqV/Gm7ffv2tz9Rl0CSJD7//PMiuS3KY5/kCMAz/To90PmVIS8jg/ONGnPYzzBYxTQvj55PNWbj4ePY+NuWOwApCxMTE7Zv386cOXPIzs7G09OTvn378umnnz5wmcaMqSIIEQShChBBSCVx9rKmmq8t5w/HE9zVq8znjRgxgoiICPbv31/i/oyMDHr16kVgYOADLQ+feOoGadeyyM/VIVmrudLGDb0so8TwYNJk5+N3MgVNcm65y64o8q3xCt/N/JNclQkFBTp0Oh3aAi3NfF14dkD3Sq+DrqCA+PBw0OtRm5mx6a+/iLnVPVJXreZ8YwUrj+3FTDKja9OuxvOmT5/OmjVrOH/+PObm5rRs2ZKvvvoKf39/4zEJCQmMHz+ebdu2kZmZSS1fHyZ+OpHn+j7Ljp07UCoM1/H09CyWLfVh6eVbGVMlMTBVEITHTwQhlSTjZi6JMRnlSlo2cuRINmzYwN69e/Hw8Ci2PzMzk+7du2NtbU1ISAhqdendPLmpGo79doYLUelIEqgUEgpJIrNAbzzGvaYNLXr5FTkvNj4TTqag1N9d4qPzvkMas1LsmJNii70mE1O9lgQLewAuhMbzbNknuhQxefJkpkyZUmSbv78/58+fJyYmhpo1a5Z43gsvvEBQUBCmBQVMmDYNgMGzBlNDVwOtnZamNZoaj73fIGOAgQMHcj0pEefXG2BrG0PS4UT6vfQiPpN9MPdwpmf1YXzV7fUHu8n70OgMi9mZqqresgKCIDx5RBBSCXIy8ln9dSjmViYEd7l/K4gsy4waNYqQkBB2795d4sMwIyODbt26YWpqyvr16+87viJ8dRThkYaxJU4WKlyqWaLXyeh0emq3csfW2xpbD+ti59nZmpKKTGaapti+R2XkBwMYmp1Lfm4edk52AIyfvZ6ViUom92/+UGUHBQWxfft24+vCMTXVXFx47733ALDNzcVKkjgYHs6mY8foX78+Dvb21L1jhs33I75n+qzpKNOVRRKQ3W+QcU5+Hjv37sZtoAsOPuY0se+JT+MgZm4bg29KazTeEhvjv0e/RcfMbm891L2WJF9nSI9vqhRBiCAIj58IQipBTPhNcjLy6fdJU9Sm92/2HjFiBMuXL2fdunVYW1uTkJAAgK2tLebm5mRkZNC1a1dycnJYtmwZGRkZZGRkAODs7FziLIrL51IAGP5j+3Jlx7QyV5MAaPMrfvpueVhYmmNheTtlekOzAlai5GZyxkOVq1KpiuUr0RcUMGPGDKysrHDLyODtW+seLQ0O5qX+/enz2WfFyrExtyGoVRBRe6O4mXkTZxvnEq93MyUVgNNp1/njny85eHMdZr4mpB/JZtdnf+FT3ZO///4bhU5m7shP8a5Zk2ZLO7A5YS4X/z5NI5fGKCUlSkmNUlIh3UqpXzimQ0Iq+loq+l2BRGEOfgmIyjgFiCBEEISqQQQhlSDyWCJW9qbGwan3M3/+fMAw4PNOixYtYvDgwZw4cYIjR44AGNORF7p8+TI1atQoVubNbEMQUd703GeOxmGHhJVrxawke68ukPgbKUTGxKNUKlEqVShVCpRKFa4ONlR3sStyzolbsYdPUK2Hqk9kZCTu7u6YmZnRokULpk+fjr1CgaxQYJmdzVvfGDKdhoaGEhYWds+FGKs7VieKKE5cCSciVYOkKOBS6nWS8+KIz47nRn4EsfPOYeFnwU+pXwJgQ0NGTnudkz+uorZXDVQqFRYWFoSEhBh/tws6LWT0rveIzj7IpSt7H+p+SyJrLUAWf/UFQXj8xL9ElSD5ehaWdmX/pHmvmSZw/9ko95IanYa9j12Zjs1NzST3eip2gOSsIOZaInp94SJzoEcPekN9ZWTDtF4Z9LIhgbisNyTklGW94bteJj4pBR9fP378dcmtskCpVHDs9EVGLw8lDpsS6/JpPWjXqiFg+FQflZSJH7l4ero80PsA0Lx5cxYvXoy/vz/x8fFMmTKFNm3a8NHzz4OdHQ1t7YwtCAsXLqROnTq0bNmySBmavHz+OXyG63Hx3Ii7jBr4+sA0ksyTihynlh1IXXEDOU7BsB/GU9e3Ln38O+NkZc2oUaNIS0tj+/btODk5sXbtWvr168e+ffuoV68ezb38ODxoPVl5Wgq0OnSylnydFq1eix694f2XZeMixzKy4b299fPtxQENvw/9rT87siyz5uQ1ftuXgKlKDEwVBOHxE0FIBSsMFqrXtn+s9Qiu78jJ08ls/uEU/ee0u+/xNyMuo1l2jcJJucP/OkcyDxb43Cnt9E1yMrS8uSnlrj3JgA1NVYm81aMROp0WvVbPiag4frmkZmo4TA0Pu3242pmB+gs8jB49ehh/rl+/Ps2bN8ezenU2XbrE282a0+VTw/owubm5LF++nM/u6IbR6/VM+m4hyvTrt6tUuA89Q2rOpHdQXcxVJlS3cWbUqFGsu3iJ00dOFhnjEx0dzdy5c4mIiCAoKAiABg0asG/fPubNm8eCBQuMx1qZqsBUBVRc18le83zszbKKLaInCILwOIggpIJpsgrIzSzAzPLxvrXN36zHyZG7SdHoSL+aga1X0RaHvXv3MnPmTEJDQ4mPj2f16tUEqJTYaA0J0sa2sCYuLoYVixdwPuIUep2O6l41GPfxVJxcq6Hg1rgDCZAk42vDs80wQkFSwJ/xloSExpP562DUJqbUCarH4DdG4urmjqSAFg3aY2N9u+unazs9Put3cDMxCWdvw8M7cusmbqQk0ad1o4d+XxLOnOHqsWNkFxRw6dIlbOzsyEpKot3btweBrlq1ipycHAYOHEhuvpaFGw9y89ROlECaZE3XLp3p1CSQAp0ecxMVk5WTjefeb5BxTo5hHZy7u8mUSiV6feVPScrI1WJjXr7keYIgCJVFBCEVzMxSjZ2rBWf2x9GkZ8lTPh8FpUrBM4MCWL/kPP/OPkn/2UVbQ7Kzs2nQoAFDhw6lT58+KBQKzPWGhFtST1taVLek2dDnGDZsGAsXzMXGxoYzZ87w1FNP4eJS9i4RdV4yfZ7uWqQLZOL4d4iIiMDauviYGaVSwcvPF13nZekfczBTKpAsrTm9cuXt7ojCLok7XiPL6DQaos6cQa9UUs3FBZ1OR0JGBnGyTF7hrCJZRp2rISMtjd7jxmHp5GS83sKFC3nmmWdwdnbmvWnfY11gaMXJd/BlzruvGo8rqX2icJDx0KFDefbZZ4mNjQUw5nXp3Lkzvr6+DB46hCwTHZHHw9HmFyBr9QS+3YZOy4aADHpu3c+tbi/9Hd0rIJOclQ/IOBgz28p3fb/T7W3Z+VosbT2BDiUcJwiC8GhJ8oMONqgkGRkZ2NraGtOS/z86fyieHUvO0X9icxzcK2aA54NaMWYPyRodg6Y8hZVryQvBSZJESEgIPhFgn+VIun8mn29ajImpKb///nuF1ictLQ1vb2++/fZbhg0bdt/j923dzI6Dh42vd+/eXSyBl6OjIyNHGlaw1Wq1bNmyhTNnzqDVavHz8aF3t254mZnzz66dNKlTh+cGDCBdo2H6998TFhbG2bNncXY2zG6Jioqidu3ajJv4JQ4u1chPuoze3pP3Xh+AjYXpfbsxStv/9NNPs2XLFuat/YtfI5ZxcuYGCpLzQSlh6mABsoTaxpSgjw0BmHSrLMPsF8n4unBfYmYeAK7Wd07Vlop8L1oTw6tkjqNAyalBYfe8D0EQhPJ6kOe3aAmpBDUbOKFUK9j710WeHdPwsfa/16rrSPLxJP6ZcaxYa8jd6ozvycWpm7E+b8vGdRt447lX6Nq1K2FhYdSsWZMJEyaUeRG60tjZ2VG7du0iC+CV5srFC8YAZFi/F7G0tCI3PZ3E+HjW/vnnre4fCbVajaOTE5IkMeaDD7h48SIvvvgiAwcO5NNPP2XfsWMcOHCAvS+/zMKtW/n6zz9xdnamdevWHD582BiAAPz2229Y2thiKeeSmhiH2qEGHwzrh61l2da9KS2mv5p2A18PLyavHI19I290aTqm/ziPj95+B4Dz589Tp04d5gSO5qmnnirTtR7EH+f+YHbo7EorXxAEoTxEEFIJTC3UtHjeh/1/R3LtfCqedUpfiK6yeTRw4tjxJFJydfc9VmVqQsCUpzm5YhvZ+bn8FPIHo1sNYMzE19hz7DB9+vRh+5atdOzS+YHrk5WVRXR0NK+99tp9jz26z9DiMfS1V/H0MUxfNbe1xdTcHP9GxceHpKens3T5cp577jlq1qxJzZo1WbRoEXXq1OHw4cOsWLHivtccM/5jUJsjS3q+nvTRQweQIWcPsPDESk7tX482L4/GDZ/jdb9uPKt9mrdffsV4XEBAAF5eXhw6dKhSgxCVpEKnv/+fBUEQhEehfEkkhDKr38EDtZmSmPCbj60Oucm5hCw8C0CTxiUn07qbQqGgWvt6ALT1C2ZE80HUv1aDUdVeppNPC74bPYNrH+3j2kf7OPNRCJrUeycPe//999mzZw8xMTEcPHiQ559/HqVSSf/+/e9bF5/atQH4d/26ItsLc33UqlWLAQMGcPXqVcCQ26OgoMA462Tbtm1FHu5lcTgiChNJR/Ouzz90ANLjm+G80KgtGwd9R+Lvcfy0dCnrR39PVko6JiYm2NnZFTne1dXVmKiusigVSrSy9oGnfAuCIFQk0RJSSSRJolZDZ07vvIaThxV1Wro/smvrdXr2zz5JeJQhbXujZq40HxpU5vOdnJxQqVS07t8Th6F1ybyehNrOHJ+LtQg9GkaWbRbanDzsCpxImH4M2ltTo2ezEsu6du0a/fv3Jzk5udQukNKkpRsCnNTUVOO20nJ9REREkJCQgImJCR07dmTHjh2kpaUB5Xu4Fz6ca7qXLWi7243sdEZt/I4z2evQ22noNOMNJj71GhvXbeDjd8fSqsHDz/B5GCqF4a+8XtaLRewEQXjsRBBSiTq8FkBmsoadv5/HsboVLt6PZqBtwokkwqPSsTFV4OlrR4tyBCBgWEK+adOmXLhwAQt3eyzcDTlPEuUs/NrWI2CCId9G5IrdmIeZkLItBkoJQsrSBVIab19fOH6CAlkiNycbcwvLEnN9eHt78/fff2Nubkjz3rp1a3bs2EFu7qNbCViWZb7au5rfo75BUmggO4h23o35btAo1CoVrZ9qxbFjx/juu+946aWXyM/PJy0trUhrSGJiYrGU8hWtMPAo0BcYV+sVBEF4XER3TCVSKhV0e6MuyLBy+nH0ukezNG3kgXgAur5Rl/ajGpZ4TFZWFmFhYYSFhQGG9O9hYWHGro3x48fz119/8csvvxAVFcXcuXP5559/eOedd4xl6CKyAEg3uTsRWcWwsTMEP3qlkjW/Ly3xmDsHurq5uZGfn096erpx/9WrVyv94X4qPobWS1/kj5gp2CtrMbvVn0SMXMaPvceiVt2O8/V6PXl5eTRu3Bi1Ws2OHTuM+y5cuMDVq1dp0aJFpdUTQK005AjR6h/v2kCCIAgggpBKZ2FjwvPvGZrgo0/eeCTXjLlseAi71nUq9Zjjx48THBxMcHAwAOPGjSM4OJiJEycC8Pzzz7NgwQK+/vpr6tWrx6+//srq1atp3bq1sQy1ZMhRETzuxUq5D2e3arz4dC8AIuMTSbh2rdgxhQNdq1WrVuThbmpqqNvSpUsr/eH++b55ZHCBTs5vs3fg73SpHcCECRPYu3cvMTExhIeHM2HCBHbv3s2AAQOwtbVl2LBhjBs3jl27dhEaGsqQIUNo0aJFpQ5KBVArDEFIgb6gUq8jCIJQFqI75hFw97PDtaYN4buu4dfEtdKvp7o1oHLTpEN0n/xUiQMsy7IezdChQxk6dGip+7PtszFNMuPq+qMEvtGj1OMeRlCTpmRkZrBlzz4W/PorW7ds5oWne9FnwGvExcUxadIk40DXOx/ugwcPJj4+nj179pT54R52OYxLV06Wq34Lj2/hYu5mAOb0HGHcnpSUxMCBA4mPj8fW1pb69euzZcsWunQx5AGZPXs2CoWCvn37kpeXR7du3fjxxx/Lde3S6PV6Bq/vSGRmCupbHzPkW3lCCm79yvO1OcDjXVpAEARBJCt7RM4eiGPX7+d5/ds2mFpUbtrsmN2xbFwRCcDT/Wvj3c6jUq4Tve4ApocMXUxpdik4tPbB7alAFKqKb2A7eWAfZ0+fZvq8+Vy5coX8/HzjQNdp06bh4+MDgEaj4b333mPRokVotVp69erF/PnzS+2OuZR4iT+2/kFGfAaWObcTy52qkUFbv7YMbdyZ5JxMFoZuZUvsSvysm7LouQmYqU3YGhnGewcNU43fC5rH4CZtH+oedTodkydPZtmyZSQkJODu7s7gwYN5Y/QrrD0zh3y9DpXCFLXSFJXSHJVCjRIJhSQTk3aWvIIcbmjS2JNyu8XtJa9gDFlX9SDr0WozkDRRjO+8CUvLh1uRWBAE4U4P8vwWQcgjUhiEvDatBTaO5pV+vfTYTJZNOwaAs50JAcHOBD7jg8q8Yhu/chJTubRoLzaptigkJSmOydQf/1yFXuNOG/9cxrELUSi0BbwzciRObtVKPG7x4sXExMSgVqv55BPDwnQ6nY6wsDDi4uJQKBScTTxLWmwasiRj4mxCTZ+a5KLjSMQxQh0OIEsl/9WQdSYo9c7olEkgK1nY+Q+ae9Uu8Vi9Xse6M98SmnCcdXGG6dI1zVTM6fQL9pZe2JvfToH/5Zdf8u2337JkyRKCgoI4fvw4Q4YMIfAlV3LamGMiGVbE1QN6Sp4+7GZiggKZBg5eTGy/ECtTxyL7MzPPcvRYbxo3+gs7uyb3fK8FQRDKQwQhVVRaYg7rvwvDydOKnsPrP7LrXt1zjX/+vGh87eViTu/PK2dsxI2waPJWxKGVC6jxVcdKuQZAzMULLF7+JwCN/GrxzICBJR6XlZXFrFmzUCqVdOrUidOnT5OYmFisCyrbPpv3Br2Hm13RlpI8bQF/R+zh8LUIrE2s6FOnHc08/Vh6cjtbLu0jKfca7pZejG/5GnVdDS0KsqznSkoYzla1MFdbEnZ9Ox/sm0BigQ4lMroSAoffO82goYdh3MvTTz+Nq6srCxcuNO5v1tGfi5pExkx9lskdlwCg1+ej1WZToM9HBvQy6FFgY+pYbGG8u2m1mRw81BFZ1uFTaxzVq/dHElN1BUGoACIIqYLio9NZMzMUgFe/aIGtc+W3gtxJp9WjuZnLsilH0Mow/Mf2931QPdB1CrRET9iChcqGNNdUarzUEiv30gfGPqgvJk1EJylo4udDz/4D7nkvc+fO5ebN28ninJycCA4Opn79+py5fIbNazbTok8LutXv9tD1yivI5vNdg1gffwGgSNDxslcwH7dfYhybcyMzhrlHP2PNtTAA2jp6UN3Smcsbcti2+jhbt27Fqbolb85vxdovrtF0cE22fn0Ea9OKybybl3+T6OhZxMevxMqqDrX9JmJvX/IUa0EQhLISa8dUQQ7VDIvGmVqqsLIrad3VyqVUKbB0s6SWuyUXr2fz8zu7eWteByRlxa5no1SrsK7pii42F7tEe9K+P0ecNpN8vcZwgCQbV4At+n/D9vmHVvLToVVFyqzh4M6aod/ceiVzKu4ia/ev4Wx8FEqFkobzf2Lbtm3G/CB3KwxQ7O3tefvttzE1NeX3bb+z4bsNmGoNvwtz5YMHhQcurWDrpTUcTY7hmuZ2TpK+ng2wM7UlLS+TLjV60arWS0XOc7auwZROv/PajVAWhE7lVGoMB5OvUVBXxuJSAf4B/oZ5a3po+aoHu2adxERt9cD1vJupiROBdWZQvXp/Ll6cwomT/akel0vtlr+jqNWpwq4jCIJwP6Il5BE4tvEyR/+5jJOnFV2HBWHv9uhX1tXl6/h19B60d/y2O3b3xv+ZmhXSMpJ1KI60ddEAKJ1Myc/KRWujRae+tf68XPS74U/d7W0/bF3G1vD9/DZsmqFA2ZBYy97SFmQIu3KWN5ZOZGjLPjxTswsqhZKjmvO8++unxum4dzt//jwbN27kzTffxNraGoBpv02j4GoB1gHWWJpZMqTXEEzV5Q8O10fM4ZNQQ7eJj4UlNW086B/wKh52dXC39S93eek5cbw85Wl2/hpB8EBPOjduhm16ADMmfs+3337LoEGDyl1mWciyniu7BxAtH6VWTDY1rbtD5ylg710p1xME4b9LdMdUYYkxGWxbeIaMm7k0fbomTXvVrLRr6fV6zuw5Qb5Gg0KhQqFUoFAqyc/WERt2k5sJueTmAUhIKHCxMaH7yBbY1HzwKZv6nAJipx5GqYfsttXw7+lbrvMnT57M2rVrjcnT7vbUU0/RpUsXvvjiC05N2YJjrqGFSYuOm44afF5pgn31+3f/fPHrF2SkZDDzg5nlqt/dWi9rQLpOz0/tJtOyRt+HKquQp6cnH330ESNG3J7qO3XqVJYtW8b58+cr5BolSdj9Jmf0O7DM1vLURXPISYEW70DrcWD23/k7KAhC5RLdMVWYaw0b+n3SlINrojm2MQadVk+DTp6YW5lU2DU2/fgnkUd3UqBJATmvzOfFZsAvH/1Iqw4v02RwP1Rm5a+TwkLN1o4uNN17A6e98RwLTcRWo0dWSMRWN6fDG8EolfducSlcmM7MzIwWLVowffp0vLy8SEpK4siRIwwYMICWLVsSdTEST5tqfNLyLWp7+eKWbEns4pPYf9LlvvWUZZlSJpaUiVav5e+wqaTfyn7bwrvPgxd2l5ycnGKtUkqlEr2+8jLtppz+nrO67UgypGQPo/fOw4QeiSN+4hRCBv7Ec+9Oh+DXQKR4FwShEogg5BEyMVPR5kU/1CYKQjdf4dSOWLq/WQ+POvb3fEBvnv8n185HIOtvjaHQ65FlveGBKsu3ZnzI5KRFolBa4+jRAFmW6TDoFSSFhF6rQ6fTotfp0eu0yDo9ujt+vnzgADkJ8RzYtYLwQ9to/9Lr+PVsU+776xXsQWNFDp0StbyYrSTZ2gT1jVwCYnLY+W8kXXqX3k1xr4XpLl26BBhaS2bNmkXDhg1ZunQp/X8cR0REBPm/XcMmU03MgfPUaBVwzzrKernUqbdl8fbm/hy5YWiV+LP7zw+90u6devfuzbRp0/Dy8iIoKIiTJ0/y7bff3jNh3MPIiF7JyZvfgSTR7qn9bNsbRoPgfIa+/gZ9+vQBt7rwz2g4+gt0mwa12ldKPQRBeHKJ7pjHJDUhm3WzT5Kdno+Vgyl1WpgSe+Yo6UkJhg/qksLwgJMUJMeGoVCYYGrljHTHdkmSkCQFSBISEkgSfs1a0P61Z0q85owZM5gwYQKjR49mzpw5ACQkJDB+/Hi2bdtGRnoGjuamdKrjw/fLNmBTs3zrrQRtPkmyqUQnjYKl3eqivPWpftPiMPwuZlIwsj513G3LVFZaWhre3t58++231KlTh1atWjFhwgS+/PJL4zH169enV69evFS7O04XDNdKbajEPkxHrpRPeoCC4FfbolTe/hQ/ZcEUsrKzmPle+btj9Ho9Pf5uTlyehhVd5xJUrV25y7iXzMxMPvvsM0JCQkhKSsLd3Z3+/fszceJETEwqrsVMlvUknvmWM0nzAWjTeCsmtj5FjpEkiZCQEJ5r4gGbJ8C1o+DfE7p8AU7l62oTBOHJILpj/o/Yu1ny6hctiItM48Dqk+xbNt2wQzLF1NLtVguHHpAxMXckuEdfWvd78Kmkx44d46effqJ+/aJ5SgYOHEhaWhrr16/HycmJn775jpnzv8fhrRf5+IelOPnfe+zK9OnTWbNmDeFnzpFvakI1/wZMXLLAGIDExMTQc0jwrYOLnvv333/z4oslrztz58J0HTsa8o4EBgYWOaZOnTpcvXqVhtPbEbn1FOY7M7AP0wFgLptgfg5C/96DRxs/VCam5OnykBNkNBYa0jRpWJtYl2sl2WNX1xCXp+FF7+YVHoAAWFtbM2fOHGOAWNFkWebmxZ+5FPM9WWoNdjlqateZViwAKcKjCQzbCmfWwLZJ8GNzaPYmtPsAzEXad0EQHo4IQh4jlYkSryBH3Gu3Y9mEf0iOjca+mjPB3bpTt0MX1KZmFXKdrKwsBgwYwC+//MLUqVOL7Dt48CDz58+nWTNDnoiv5n3Hz8uWcC01nd8nj2XgZ3Owq+GO0qLkT+J79uyhYbd+XB5ei955kPjPQrp27crZs2extLTE09OT+Ph4tu++ROD+JC62cOZi9A5mzpxJjx6lrzdTuDDda6+9Ro0aNXB3d+fChQtFjrl48aKxDN8u9Qk/ug2HLHOy21iSdiGWakl27D93jIwLe4uc53w1m+/fehmdQkZWSqBSgEqJZKZGYWaCyswMEwtzTC0tMbe0xsLSBlMLS36/sIDAbAtGv/BVuX8Hj0t+QTYXb+zHJusqiZfnkqHOwi5fRRPXd7Ht8C6UpTtJkqBuX0NLyKF5sH82nPqTxMbvY9XsVSxt7Cr9PgRB+G8S3TFVhCzLXD93huMbQ7gUegxHTy9e/GwaFjZl6764l0GDBuHg4MDs2bNp3749DRs2NH7a7tq1KyYmJixduhQ7Ozv+/vtvhg0bxqLxk4g9d/vh3eWZt6n7ck+uJGZxKi6diLQszublc06pJ95U4gWNiu+7BZKcnIyLiwt79uyhbduia6mcmnIAx1w9XdaPoGWLpkUyg77//vv07t0bb29v48J0YWFhnD17FmdnZ+bMmcOkSZNYuHAhDRs2ZMmSJcyaNYuIiAjjujGFzv+2FYsLpsRJseg6OKDX6tAW5LNm47+s37KZGzeSkQF3N0e6dWqKXy03CvI0rFq3n6jLCWRm52KiUuHtbEfvunVwsSmao6Phsw3p9ErRYK68CluQzp8/j7m5OS1btuSrr77C37/803tLczDqN3KvTjO+ts6R8HEdhEOjCUjKe3/+MHbHPPdc8Z2ZCRT8+zHTznkB0LNnTxo3blyk20sQhCeP6I75PyZJEh6BdfEIrEt85AVWTfuMPz97n2ff/xQnzwfP2bBixQpOnDjBsWPHStz/999/89JLL+Ho6IhKpcLCwoKQkBC6du1KyqVXuHExmg2LZrJt/QJesHMiw8LQOmOnl6mDRE+FKcFWVjzX1gOFQkF6ejoADg7Fs3uaPefD6dkbOH8unA/Hf1Fk37Vr1+jfvz/JycnGhekOHz6Ms7MzAGPGjEGj0TB27FhSUlJo0KAB27ZtKxaAAGgv56CQzPHoUQ/3NvWMM06ylCb0eL4vfn5+yLLMkiVLmDlzJidPniQoKAi76j8TEBCAl5cXKSkpTJ48mT/DwggPP0lOdgbXY44SsWEF4ZtP0KxHPNb2Ja9bUxZ79uxhxIgRNG3aFK1Wy8cff1ykBakiXLy6FE/gWLaaNJ3MxC4HsbB0vO9591OgV7A40vAPjKujLf/++y/Hjh2ja9eu+Pn5PXT5giA8OURLSBWVEHWRf+Z8hUKh4KXJM7ByKP/DIzY2liZNmrBt2zbjWJC7W0JGjRrF0aNH+fLLL3FycmLt2rXMnj2bffv2Ua9ePQC+/XUh8rYQrvR7nx5+talf3RZ3B4tiM0P0ej3PPPMMaWlp7N+/v8Q69es/hOPbd7F32O9c7VCNlt0qfpBjWlQccQuPYyPbI8t6cuvpqf1qhxKPdXBwYObMmQwbNqzYvtOnT9OgQQOioqKMwU5K0nmWjBtHjaY1eH70jxVW5xs3bpTaglRWiRlRRFz9i4KCVG5mXqSa9gwZWKOSu/PxtY0cf+UopuqyZYi9V0tI5K4/+WPPBbrVdaLFCyOJi4tjy5YtXLlyBV9fX7p27YqLi0vxQgVB+E97kOd3xS8iIlQIN9/avPDJ52jz8/jjk3GkJyWUu4zQ0FCSkpJo1KgRKpUKlUrFnj17+P7771GpVERHRzN37lx+++03OnXqRIMGDZg0aRJNmjRh3rx5gCGwkLeFADDn+bb0qO9OdUfLEqemjhgxgoiICFasWFFifXJzc9m6KYRnhw0BwGtXPOevpJb7vu7Hzted2pO7k+GXTbaciXRKwz9vTSFqy0HjMTqdjhUrVpCdnU2LFsUX9cvOzmbRokXUrFkTT09P43YHlwD8O9bk0qErJF07W2F1vlcLUlloCrI4cbQbJP2GOjUEi7zzxCtr07HFJmNeFMV9EqRkZWURFhZmTBh3+fJlwsLCuHr1apHjYqMM9930mTcAcHd3Z/DgwfTr14/k5GTmz5/Pxo0byc7OfqB7EQThySGCkCrMvlp1Xpr8FbqCAn4b8zaRRw7e/6Q7dOrUifDwcOODJSwsjCZNmjBgwADCwsLIyckBuGeCrBu3Ho4lHXenkSNHsmHDBnbt2oWHh0eJx6xatYqcnBw+GvM2lzq7c8NcQfyaiyUe+7BUpiYEDuuOeSdX1JIpwfYdMdlZwJHd+7GyssLU1JS3336bkJCQIrNufvzxR6ysrLCysmLTpk1s27at2PTYDi9NQm2uZ/cfX1dIXfV6PWPGjKFVq1bUrVu33OfvvfgTG3Y1xEQBOba9adLqGH26XOTVdpuwNa+G3tjWee9Gz+PHjxMcHExwsGE207hx4wgODmbixIlFjnN2dQXgp68+RqfVAoaWk8DAQEaMGEHnzp05ffo033//PQcPHkR76xhBEIS7ie6Y/wOa7Cz+mvQhqfHXadi9N82f74e5lfUDlXVnd0xBQQGBgYFUq1aNWbNm4ejoyNq1axk/fjwbNmygZ8+eAMz4ahrqE4cAUD7Tn5Evv4zq1iBEWZYZNWoUISEh7N69+55jAtq3b4+TkxOrVhkWqtuxKxr/LXHkvVMXH697T/csyMpHk56Hqa0pJndkmZVlmdz0PBSShJltyWvA5GfmEv3VDiwLrHH8uBHxNxJJT09n1apV/Prrr+zZs8cYiKSnp5OUlER8fDyzZs3i+vXrHDhwADOzojOVtv3+Aac3nmHAjM9xq9G42DVTs6+xKeILdBlHcVdmAHBF74y1nImZVRD1a0/A29HwsB8+fDibNm1i//79pQZwAHkFWUQm7eF6ShhZmjg0+ekU5ETgrsgkTbbAqdrLdAz6pNh5ITs/YmLsRsJeOYZSXTEzri6s/44/T6TyfEMnGjw3stj+7Oxsdu3aRWhoKHZ2dnTt2pWAgIAKTe4mCELVItaO+Q/LSU/j6PrVhG3ZgEKpot2rQ6jfuUe5/1G/e0xIZGQkH330Efv37ycrKwtfX1/ef/99XnvtNeM5BQX5LFzxJwnnzmAdfZabbl60fPEVerduzTvvvMPy5ctZt25dkZkdtra2RVa3jYqKonbt2vz77790794dgNw8LdHTDpNub4qHvxMKcyXV23igUClIi04jdlUkyhQNaglMZRnFrXvNlkCrUqLS6ZG0eswljO9D9emti7wnsixz4e+dWJ00QaPLwXdm0VwrnTt3xsfHh59++qnYe5Wfn4+9vT2//vor/fv3L7JPk5POT+/0x9FPjUMvf1QKNTfTIyjIS8BEaYKLHGc4Tg9mCojVmmOqNMVFSgNAL0OibMffv0qE7o9j39791KpVi7ScBBIzzpOvzSQt6xI3Uo9yOfkYzio9zioZk1uNUQWyoRkzXW+KyrYt3RpOx8Kk5EBu9baPmBq7jROvHUR6gMX6SiLLMn/PGsO5bHte7RKMb6tnSzwuKSmJLVu2EB0dTY0aNejWrRvVqj34gF5BEKouEYQ8AXLS0zjw1zJO79iMlYMjtRo1JaBVOzzq1H0knzJ/Dgkhc4Vhau3o5etQq0qeYLVo0SIGDx5sfP3xxx+zbNkyYmJiinTr/PXDMVpd1xhff3v0D2bv+okhjV/gk87vclmRSpfpJT/gvu43lW5NumHpZY3pySQAPGYUTTcftWofZscNPxc0UVLzhZZF9nfs2BEvLy8WL15crPy8vDzs7e358ccfi9xLoX8Xf8T5LeEEvRqJwkxHtl6BtVJPklaNicqcPLUnvZv+irVZ0UGaKdlxHLz4A99P/pUTB1KZ9Y07jtVNkJGwVBRdJyZVqyBZCwlaBfWqdaC6Yyt8XdrhaOVF4V/duOREjh87T2xsInmxKjDTgpkOOUONnKnEIseOAkUeo2a3R2latoGpZZGfmcyX3/wAwPvDh2DlWvosrsjISLZs2cLNmzdp2LAhnTp1Mq5sLAjCf4OYovsEsLC1o8ubIwls25GLRw4Qffwwp7dvxtHDi3avDqVGw8aVGoy8+fzzTD56AOtL55kxtD+HIs7wVFDgfc/78ssvi6RcL3QhPZ8EJQx5oz47Q3bx94l1BDj7oLVSU+39JtSwNyH+3fgi5/z888/MnDmT4QtHY2VlyOFxPiMfk6jig1zNHKyBTHJ12cz49mdeNnkdv/qBZGZmsnz5cnbv3s2WLVu4dOkSf/31F127dsXZ2Zlr164xY8YMzM3Njd1Sd6vWsAfnNkWQaf0uHZr2w8HMGb2sR1GYWr8UDpbu/PtLOkf3FPDz77+gs4wgOS+ZfH0uzk41cHOui1pphYOFJx2cGhERHcmWVcc5fSCfKHc9BxX70en1mJ9zL1KuXrIj1zUe8iRU6WYokZCc81BlpUKyPblpGqxcKy4IMbF2pKZJKpfz7Um9evaeQYifnx+1atUiNDSUXbt2cebMGVq3bk3Lli1Rq9UVVidBEP6/iCDk/1T1gECqBwTSfuDrXA0/xd7li1gzYzI2zq68/v0vSPcYRPqwJkz5khlfTMTqYgQHPv+A6GHvMqBr13KXEx6dguO1PCyf80ThpOSD+R+weNXvTJ06FYeGLlg4GR6Ybm5F17AJCQmhX79+xgAEQKEs+aHv0bEh1/SnMN8O2Vl5DHl9KDfSU7G1taV+/fps2bKFLl26EBcXx759+5gzZw6pqam4urrStm1bDh48WOp0U73ekCLexcYHR3PDMUqpbAm75s83rNvy0jNFF6dbtGgRzzV7lSEj32DJjwtpX7cPL7QagaS2wsw6B66YoJUVyEqd8Ry7ntkEBwXi4+WJqbp4ZtvINevZuhV+n3SUll57CRo0AFX1+weO95Ofm8PVfBtc1Dm4B3e+7/FKpZJmzZpRr1499u7dy549ewgNDaVz587Uq1dPjBcRhCeQCEL+z0mShHf9hrxabw57/1jE8X/WsOH7mTw9+oNK+0fd1MSESV/MYNLnk7A5E0rctdgHKudCrGHmjVqtZMSIEfTq1YvOnTsXSy1/p9DQUMLCwoxTiO9U6t3eigtm9fwIizdq4ODjWewQd3d3/v3333LVX6czzPpQltIldS9394Jqcgo4eOIkx7dd4p0BX/LPho1Ud6iFzj4b044pvNy9I4425Zu+m5+RRs7FUKySttHPcR/O6suQD/wyF5wDoNuX4Nup+Im5qbDnazi/EbISQdaD2gJeXQUeTZH1euIj9hF99iQ6lPhVd0CpKntrhrm5Od26dTPmsFmzZg1Hjhyhe/fuRaZDC4Lw3yeCkP8ISZJo9+pQ7Fzd2P7rj+y2s6fD4Dcr9ZqvvTGc1e+9jWpLCD9npPLmmPfLdf7TLb2Yt/ka22b8xs6ow0SeO3XfcxYuXEidOnVo2bLo2I57jWxyb1uP8//+g0KpxKOEAORBFU5PVZRjEbySZGmyWfDxNsw1NqgKrFm59QeGjX2Hw7u206hRAK/3e6Fc5aWeOkzev9Nw1hzETtJiB2SZVifO+z3sAgKxuPA7XNoDy/qAlRu0+xAavQZnQgxrwyRGgF4LkhIsncDKBRLCDQvYDfmXk6tns/5MJgBKtNRp2PyB7tvR0ZGXX36Zy5cvs2XLFhYuXEhQUBBdunTBzs7ugcoUBOH/iwhC/mPqd+5BVmoqh1f/iWdQfXybPlVp1/Kt5sawBUtZ8sYrZB7azfpGzXmmbZv7n3iLmYmS597yZFL9+YzoOYNriRp8vUufQpqbm8vy5cv57LPPiu8spRmkQJPHlY1HsVE6kGqXUua6lUVhS4iqHK0AJVm87B/MNS6keV7hdMRWXnutP19PmUj7PTvLXdbNw7uw2/QiClxJqDUWk6DOWNfyw8rBEWPnVfMXIDcNNn8IEath41jDF4CkAFsv6DwZ6j5/u+DJtnDlAAXha9lwJg1Q0twdOvUbhYmd60Pdf82aNXnzzTc5deoUO3bs4IcffqBFixa0adMGU9OKmc0jCELVJIKQ/xhJkgju/jSHV/9JfNQFYxCSl6/h3LXTKLQy508dQmFjzgvd3nro6znZ2OA/agKnfvmOyHlfMf3cGSa89Xapxx+5msrJa2n4WpiBDPt37SQ7K4VZK99m1qrhSJIhm+nevXuZO3cueXl5xoXRCpOdDRw4sFi5sgxqSeJm+A0khYSkUJC4Kwx1TAFmCgvSSca1Z9BD3++d9DrDuAylsnxBSFaGhtDQc5w8cRHtNVPMc13IskymmofEihURLFm06IHqE7trJw67hpGmqo3te1uxtbQq/WBzO3j+J3hmHuyZAXtnQuCz8PzPUFIukaavw/FFqFcPoif1OKxoRqcBkzCxfPgFFsGQCC84OJjAwEAOHDjAwYMHOXnyJB07diQ4OPieifIEQfj/Jabo/gclX49l8bjhOHnVwLt+MOFnDpJ/ObHIMTvORRF2M4n0DA2WFlYlruKq0Wh47733WLFiBXl5eXTr1o0ff/wRV9fin3w1BQX88FofFLJM3+9/o4ZryYM5Z767C4v823/kNPk5pGQZ6pZpqeDN95oyZMgQAgIC+PDDD4tkEL072dmdIv88j/mpG8W2pxak4vxybdyaBtznXSsbWZY5HX6BRTsiiL92lbwcDe+P7Ujr2qW3OF1NjCMy5gp5mnwijsRicckNCQXp5jdQemjw8HOgcYA/7dq0uec6P/dyefFsvC5/QZa6FuZvr8XEqfSkZw8sLxOme5CPivx3wrByqbzxG2lpaezYsYPw8HBcXV3p1q0btWrVqrTrCYLw8MQUXQEAx+qedBzyFqEb13Ip9ChpeTfIstMQ2KM7uWnp6AoKiD17gZY1PfGytUXjY8PZs6nFVnEdO3YsGzduZOXKldja2jJy5Ej69OnDgQMHil3TTK1G16EXip0bOBEZWWIQkpKpMQYg/m8G4GRvTuHYWVkvU7u6DRamKiwtLXF0dCwSgERFRbF3795SB4/WfMGPm3726At0yHoI3XiGzHQVL8zqhqn57Sb9+IQbnLuSRG0vV6q7Od538K5epyPi4hVORsax6VQsYZmmaBSmSLIpKmUtCmzVfLs/ssQgJCcvh9///Jfco5ao9YY6mOOKskUK9Vt4E1zzKSzUFgCsXbvWuM5PodJahO4kawtI/H4wNTM2cN3mGdxH/4qkun2/kydPZsqUKUXO8ff35/z580XLkWV69uzJ5s2bS124DlNrrqu8qa69wrXDf2L1zAf3fO8ehp2dHX379qV58+Zs3ryZpUuX4u/vT9euXXF0fPiVgAVBqBpEEPIfFdy9N8HdewOw7Owyvjr2FZeUR1g5eCUAI96YQU52Jn/+MZMbe0Kxt1Ox5+pVtu7axPNPv0B6ejoLFy5k+fLldOzYETBMH61Tpw6HDx/mqaeKP3Sf7tmbLTs3cPmH6fxrMpGezZoV2X8o+vaYDD8PG7xd7tFdcJfffvsNDw8PupYyFVilVuLW5HYLTcSZM8SekIs041+IvEK3hRG3XsVQMz+ed7sHoZRkLE3VXEpI49z1VPbE68lHhbWigEy9mkyFYaqwjU5Jd3cd9XwdeL59AxysLXhpye8cOW/N6bir1HGthiY/n7gbiVy9ksjJtdexzHYg2+8SLToEUsPFE2c7B6ytLIvVv3Cdnzvd2SJUYgAiyyR/3R23/ONccR2J11tflDg1OygoiO3bt99+r0qYzTNnzpwyzaayfPlXWNaFWiemkegegGuTZ+57zsPw8PBg2LBhREREsH37dubNm0ezZs1o165dkYy8giD8fxJByBMgKi0KACt10Ye+haU1w978nNQXk/hu5ocAHJj7AzEXdqM1caagoIDOnW/nfwgICMDLy4tDhw6VGITU9axO/tTZbJwxmYN/LSsWhHSt68bPGD6BV3cq/iAutHv37mLbSkt2VprMlFxkWYna1DBeY8vhs7wdEo0SmU+amLP6fCZnqMbYnUUHq5ro1TSw0lDNvICUXB3WpjItA11pXd8Hbw9XFHcFA98815OO0w/w2cyztCkIw1x7x3tsBfWG2tC22ev3ra+1tXWxxetKahEqJOt0XJ3zDt75x7nq/QHeQ4qvGVNIpVIVy7Vyp7CwML755huOHz9+35Tqdr63f6c5UQegkoMQMIxzqlevHgEBARw6dIj9+/dz6tQp2rdvT5MmTUoM0ARB+P8ggpAnwMjgkRxLOMbZ5LPk6fIwVRadcWBr68Tx0zdo2rQx7q390R+NIezKAZQKBd9NHoKJhxNmphZkZaSSp0ll8641jB07tsRrNfLzY1dWOhZZ6VxKSKSW2+3WifPXM4w/l5Jb7KFotVp2LtrK5VO5aLX2WNneBCDhZhpvrb0MkoLfnvWgXYsG5M2fT8yKuVy5chVZlvGq6cMnn37GgJf6IkkSCQkJjB8/npBt21iamYm/vz+ffPIJffv2LXJND1tHnnF3YM31dFp6KbBwz8DW3pLc7DyG9XoaM5OKWTCuCFmmYGoNvOUMEgM/w6vfvadGR0ZG4u7ujpmZGS1atGD69Ol4eXkBkJOTwyuvvMK8efPuGagYL63XGSciKVIukX75JLY1gx/2jko0ffp01qxZw/nz5zE3NzeOWxo1ahQ7d+5k06ZNvPrqq0RFRRU576233mLBggWVUidBECqWCEKeAE7mToxuNJr39rzHDyd+4P2mRR9aI0aMICIiwriKa16Bhi9nTITQcJRqNYoTceh1EqZKGROtgvyLccyYNpRxH8zH5I4F0Rb/vo7Y3ZuxuPV6+dqr2IedIc/djAJ3c/Licihrb/7169f58MMP2bRpEzk5Ofj6+rJo0SKaNGlS4vF6nZ7F41eTl+uMqXkO7n65tHulK3kFBXy8aAdgRh+nNNq16AUYmvlnzpyJn58fsiyzZMkShg58heB6JwkKCmLgwIGkpaWxfv16nJycWL58Of369TMud3+niUOasmXqDk5orPjrpXZlvMP7K6lFKC89g5xvWmOvyCDTvC6u9wlAmjdvzuLFi/H39yc+Pp4pU6bQpk0bIiIisLa2ZuzYsbRs2ZJnny15fZ67SQolcR1/QHdsEd5JW8lacoCLXs9i3/Bpco8uxb7PTKxdvB7kdovZs2cPI0aMoGnTpmi1Wj7++GPjuKVnn32WZs2asWLFCho1asQbb7xBu3btsLe3x8LC4v6FC4JQJYjZMU+Qp0OepoFzA6a1nmbcNnLkSNatW8fevXupWbOmcfvOnTvp1KkTqamGFOdgaBZ3r16NIC9bunv5kuWi4qNvV2B6a0rn3M9mk3dxByCRW+01VKZuWKcbcmlk2iiRAZsMHekuJnz8eetS65mamkpwcDAdOnRg+PDhODs7ExkZiY+PDz4+PiWecyjkMCe25FC3jYJ2A9oDcDE6lq6/nAbAveAG26f2x8K89LwTDg4OzJw5k2HDhmFlZcX8+fOLrCbs6OjIV199xeuvF+9embHoJAsuxPH3a01pFlTyzKCHJet16Ke4oJS0RDmMwGfkF0jlTJSWlpaGt7c33377Lc7Ozrz33nucPHnSmAJfkqTSB6be5fr2BVTf/2GRbelYoXt1LZYuNTG1cSpX3e7nxo0buLi4sGfPHtq2bQsYZhB5eHjQqFEjsrOzadmyJa1btxb5RQThMaj02THz589n/vz5xMTEAIYBbxMnTqRHjx4Axibsbdu2kXmPJmzh0Yu4GcGVjCuMaTQGMAxqHDVqFCEhIezevbtIAALQuHFj1Go1O3bsMP7+Lly4QHxcAmtWh5CWe4nwBX8wZ/ALeNQOxNu/Hp17NmV73HnyspPo+2Jt6rRq+EB1/eqrr/D09GTRHfky7q5foYL8AjRZuZzYchOJFKzyrQj/YyOJMdHMvq4BmyAaKm+w5svXSs1sqtPpWLlyJdnZ2bRo0QKAli1b8tdff9GrVy/s7Oz4+++/0Wg0tG/fvsQy3n25Ln9PTuCLNRGsD+xQ4Snz9Xm5pM7sjqOk5XL1D/B9o/QxIPdiZ2dH7dq1iYqKIjw8nOjo6GLZSfv27UubNm1KbIm5U/XOb0PHN0k7u4O0qKMUXD+F340tsMwwjihR5UGOS2OsW7+BU53W8JDvSXq6Ic2/g0PR9PVbtmxh8+bNWFtbs23bNnr06EGvXr3EejSC8H+gXC0h//zzD0qlskgT9syZMzl50tCE3bVrV9LS0pg7d66xCXvSpEklNmGXRrSEVDxZlhm2dRjJucmseWYNSoWSd955h+XLl7Nu3boiuUFsbW2Nsw6GDx/Ov//+y+LFi7GxsWHUqFEAHDx4EIBdR9ezaMMsmsl1UCZmk5OeZixHkiyo13AETu4u2LlbIJmqQC9jU90KWy9rlCZFA4LstDwyYjNBo6VN3zY0D2pJWm4yx84epXr16rzzzjs0sPEjdHc07rUd8Qry4uKRK9y8ZgKSob55GcuRdQkAqCQTDtk25LB9Y4b5wGdv9Cr2voSHh9OiRQs0Gg1WVlYsX77cuGJuWloaL730Elu3bkWlUmFhYcHKlStLnZ0DMP+vM1w6GUdbGyt6jG+GSl0xAyZlnY6crxphmR/DJb+Z1Brw4On4s7Ky8PLyYvLkyfTr14+bN28W2V+vXj2+++47evfuXWrgdy/X53ShetpR9EjE2TbFMf0U5uSRoXQgr99fOPs3u38hJdDr9TzzzDOkpaWxf/9+4/aff/4Zb29v3N3dOX36NOPHj8fDw4OePXvi6elJjx49cHd3v0fJgiBUlAd5fj90d8zDNGGXRAQhFW9rzFbe2/Me8zvPp5V7KyRJKvUT4qJFixg8eDBwO1nZn3/+WSRZWeEAxoTsBLqt7sYnzT/hxdovkpl8k+thEUStP0RsWhJqiw7kY13sGhLgZSIR+Lwv186kcO1SOqk5WuP+Mb92B6BjvRd4yrcdN7KiWbj9O/q1fpfmfu1BMkWSFCBrcKiWi5lZAalXo0hNPkz9+p3xa9+aanXr8Pbco+zKzGHb8Fb4edsVq0d+fj5Xr14lPT2dVatW8euvv7Jnzx4CAwMZNWoUR48e5csvv8TJyYm1a9cye/Zs9u3bR7169Up872RZ5sKEfVghkW6iwHt4Q6yqlT4LqCxkvZ7Er57FLW8vNx2fxnHksnJ9un///ffp3bs33t7exMXFMWnSJMLCwjh79izOzs7Fji9Pd0yp9Hq4NVU4LzOFmE3fU+vsDwBkDNyJY60G5S5y+PDhbNq0yThuqTSF3Yg7d+7k7Nmz3Lhxg0aNGtGpUydj/htBECrHIw1CCpuwBw0axMmTJwkMDKRr166YmJiwdOlSYxP2sGHDOHXqFL6+vpV2E0LpsvKzeH7986gVapzNnYm4GYGJ0gQ7Uzt87HzoUbMHnb07F5sxU5o7u+QK9AVIbhKtB7fm34//JfrgWQLalPyA+WXWrzzT/TkyrmcRu/ca527mIQNqCarZm+LpZ4ejry2SmYparbxo3LgxK75ZTeTBeNJv5PLbjjlcSb7Ipz26cz05Cklpg5OVNTm5qWTnp6GS1ATVa0+7cW+jvjXuo/NnW4gqMAQ3MTOKt4TcrXPnzvj4+PDBBx/g6+tLREQEQUFBRfb7+vrec+bF1o92Y4eEk6xABlQdPPHuXqNM7+2dCjIzSAvdjcXu97Ekkfjan1Ct//hyd2m8/PLL7N27l+TkZJydnWndujXTpk0rdWxNhQQhJUi5cBCHP3uQYFkHt/GHy3VuaeOWSpKdnY2VlRWbN2+mc+fOHD9+nF27diHLMu3bt6dZs2ZiSq8gVJJHkjH17ibskJAQAgMDAfj777956aWXcHR0NDZhh4SE3DMAycvLIy8vr8hNCBXn7e1vk5Bt6KKIzYylr19fatrWJDk3mVM3TvHRvo9wOe7Cp099SnvP9vf9lO3h4cGMGTPw8/MjPjOeFya9wPbJ2+lj+Sxzkt4n/Kvt2L9QG6WFIT/Hzz//zMyZM3n5rZewsrLCJcgR367eNE3MJvN6Ns51HFCZF/1jWK1aNYKCgvBu6Y53S0NTetL8cKZOPcTzX00mfPkGEi5HosnPwtWjJtX9A6ndow0mNkU/6dqolFCgpaz0ej15eXnk5OQAFFuvRKlUotfr71mGqUpBorWKOq/WI/bncEx3xxJx6ga+b9fHzLZsgV7Cwg9xi11AYTtFjuxAtVceLDvpihUrynV8ZY1Tt3CrDUCWa9lX3L3fuKWShIWFAYY/Q0qlkubNm1O3bl127drFli1bCA0NpUePHqUGYYIgPFrlDkL8/f0JCwszNmEPGjTI2IT92WefkZaWxvbt241N2P369btnE/b06dOLpZUWKk6Pmj2QkIjJiEEv63k54GUCHG6vo3Ip/RJfHv6Sd3e9y3uN32Nw3cH3LK93797Gn710Xri+4ErKrhRCw8NQt7cl6JWGSKrbD++QkBD69etnnH1RyMrVEivXkpvHW7VqxYULF4psu3jxIt7e3phaW9DkrX5lunfTWwFVK+viUzYnTJhAjx498PLyIjMzk+XLl7N79262bNlCQEAAvr6+vPXWW8yaNQtHR0fWrl3Ltm3b2LBhwz2vqVJIKBUKrKtbEzDpKaL/vIDV6RtcnX4U675+VGt6/1wcbrG3W1ry3o3CwqF4t8n/m4RNs6gBWNmVfcbMiBEjjOOWrK2tSUgwBNOF45aio6ON43gcHR05ffo0Y8eOpW3btsb1d8CQ9O3pp5+mcePGbNq0id9//x1/f3+6detWbJCrIAiPmPyQOnXqJL/55ptyVFSUDMgRERHF9r/11lulnq/RaOT09HTjV2xsrAzI6enpD1s1oYz0er38xaEv5LqL68of7PlADr8RXqbz4jLiZI+3PWRJJcmfffizvHFbpKzX6437jx8/LgPygQMHylWfo0ePyiqVSp42bZocGRkp//HHH7KFhYW8bNmycpVz6XKK7P3hBtn7ww3F9g0dOlT29vaWTUxMZGdnZ7lTp07y1q1bjfsvXrwo9+nTR3ZxcZEtLCzk+vXry0uXLr3vNQ9M3Ctv+PpgkW3J55Pl8x/vk2M+2COH/3Ja1hZoSz1fm58vp00MlOVJNnLsD++W426rtoxr52TNJCdZM8lJvrTgFTkrPvK+5wAlfi1atEiWZVm+evWq3LZtW9nBwUE2NTWVfX195fHjx9/z3w69Xi+Hh4fL33zzjfz555/L27dvlzUaTUXdpiA80dLT08v9/H7ogakdO3bEy8uL9957j/r163P27Fnq1Klj3N+tWze8vb35+eefy1SeGBPyeGi0GpadW8aayDXEZ8fzSfNPKNAXEJ8dT13HunT27oxCMrRw3NklJ5vIeL7tiUWvv0lRmNA3U2JaK1/yNDpGjhjB0WMHmTVqKQW2pnR+oQ4uzmUbHLhhwwYmTJhQZKG1Ro0a8cMPP9CsWdlmWGh1enw/2QSUbUxIRTg4aT8plkqe/qBF0bpotEQvOI1lQjbJpkpqvdMA67tagtLOnCAv5ENctUeJdRqExztzSlwLpqrIjzhAwcZZSJqbSPX7YP58yVl0C2XER5O2ehxeN3cDEG39FOqWb+PV4vlHUNui8vPz2b9/PwcOHMDCwoIuXbqIKb2C8JAqfWBqSU3YX331FVu2bKF9+/YEBgZSrVq1Ik3Y48ePZ8OGDcapj5VxE0LFySnI4d2d73Ik4QgKSUE1y2pcz7pOA+cGvFD7BZ7xeQZtgdY4q+TnZT+z+LfFOE19E13dYZjrZOzzZOYdSKX7t88xouVAnm7WDy8MD9NQX0uefb3RfWph8NdffzFw4EAWLFhA8+bNmTNnDitXruTChQu4uNw/Idj58ES6/3Gcpko1K6eVPrW2Iu2fsp8MUwU9P2pZ4v7rW2LI3xmLFjDrUQPP9p7kJl4nZeV0qt34Az1KbgTPwO3ZYVX6gahNjEU1//aaNrIMOaYtUTV/Gd3FQ2BqiXn/KUjmxRcpjN29GH3kDryvrwcg5eV/cQho9cjqfqfU1FS2bt3KuXPnxJReQXhIlR6EDBs2jB07dhAfH4+trS3169fnww8/pEuXLoBhjYqPPvqI/fv3k5WVha+vL++//36RKbuVcRNCxYvNiEWtVONm6cb+6/tZcGoBp26copplNV6t8yqvBb6GJEnMODabL4Z+heRRG6sPvudHTytGX8rAImQdlxdO53LMVaq7u3EuLAGzFRdRIxHlZUHL1xtgYnLvIUnNmzenadOmzJ07FzAMHPX09GTUqFF89NFH972HyfOPsPiKIQ9GcxMT/vq8y8O/Mfex5/MD5Kokun9cchACkBmbybVfTmOVpyPLPo0aOSNQUkCy56vYvzgRM7uq++c+d9VXyCf/wkQfg1KtI6/DAkxbPk/OD69ikbmtyOQdvU6BxnsgJp2HoapRv1hZSVMDcdFeJ2XAdhz8mj7Cuyju0qVLbNq0yTilt2PHjsXGMQmCcG+PJU9IRRNBSNUVlhTGkjNL2Ba7lzTXz0AyQ6t2I2Xcm5i4OtPm0zf5u3k3XtsTxb/vDaa9twdb1681nl+Qp+XggpP4xGtIV0JCE2eC23hha2uGUl202yE/Px8LCwtWrVpVZLrooEGDSEtLY926dfesq0ZTwIWTCUxfd5bDaKmrVLFhWreKfDtKtHPqAXRAl0/v/clep9VzcWEE3tfGY6E8THaTb7B8umy5dMpDl3qD/C+b8s2xG4REKrmYko+5lTUtW7Xiq6++KpKo7ueff2b58uWcOHGCzMxMUlNTi2RT1cbHoPrJMAU7R9kYde+PUTe8vcpyQVQY+XtWoGrQGe2R1VjeWG7cl6NujMUnO4tWbrJhOYDYdt/h2WFwhd97eel0OjGlVxAegghChEo3YcIE5CB7Firros/JxmbXn1xZvpbNmzcbs4nuOhlOx8YNqPHZHCInjkSlLBpg7JxzFM8EDebc/tisQSZXgiQHE+r1C0CrzqF69eocPHjQmEod4IMPPmDPnj0cOXLEuC03J5/oMzdJjExBG5eFdXoB1Qpk1EhsJ5/JaNj1TitqetlV2PswY8YMJkyYwOjRo5kzZw4A0dHRDHpmKBExp9Ar9XTv3p0ffvgBV1fXUsvJOHYUsy1jMdFGkGfTCdVL36CsXv5MpYV0iVfQzmqOTuGMztwds9yjqM31dFuSw8v1VTSrriQvT8HHB8w4l6Xi7IVIYxKvOXPmoNFoAMPvOTU1FbM9S9CFb0RKicbENAWVqZ7cuh9h/sKE+9alIOoUusQYTP4dhEItk59nhfTCr6gb90Cv06H/whkVOrKG7MHKu+ED33NFy87OZteuXYSGhuLo6Ej37t3LnOdIEJ5kjyRPiPBkS0pKYseff5ISF4dsaUVw40b8vGWLsUsOYNvKP7F1rUZO29YM232eJZ0Ci5TRcYxhYOnVMzeIjUkjL7uA/JwCdFkFeF7LIX9+OKH1Sh6QWZCvIycrn53LwtHGZWOTkY+bFuyQsEQmTiWRYasmt7olzj4OnIpOhNPxODhVXLbMY8eOsWDBTwQF1SMhPpPV3x7GpJqa0Z/0o7qFJwtenk3QmCZ89tln9O7dm8OHDxfLOVLIpmkz5Eb70Kz/BfWp6Ui/NCfPbwQm/T5CUv+PvfOOb6pqA/Bzs7r3LqW0tFBoS8veIHvKEAVFFAQVQVCGIqKfgANFceBABAegiCgbEdmUPQtlrxZKgbZ0r7RNk9z7/REoVFZb0qbAffzFJOee+573kjTnvee8o5RF2CSJ/F8nIcSux0Z5HqUdQAKQgKgWKGz+BeunvoQxMxXDmf3o133J/G4H8fk6lT3Da9PEyREHt0TGKsFQqGDjGQ0AhZOCcfbSYyhUYbR2oMi+MYZmg7HpWLrtVXVwJOrgSIwhp0l6qS0+9a/C38+gdV6P9s/heGIk0bEB3r517i2sErk5pHfdunUsXLhQDumVkakg5JUQmXLRfO9J4guKiG8bgbXy9hNsz03HiBYMzKjmzfN1fEold8vK09Tem0q2sYjIL7vy0ZRZSGJt6ipUVNNJfLr6Y3IK8/jhyY+5ohbQumhQ+9rjVcuVoFB3bGzUJeS98VcMyw5d4djULjhYq+8w6r3R6wxcOJ+F9GM03ee/xLQu4/lm96+EeQYztdPrbLuwn8FL3uL4mH9wsLLD9+NW5Obm4uLiwoYNG+jUqdM9xzBmZKBf9D+sUv/AqKyO2HUGmma3biFJRgMFS79ATDiMMmkHNo55xccKC90RfVtg+9pvoC8AlRXcpnDf6b07qNuiLfuGBBLpko2xSImthw5JhK0XjHRcqOXKhAY4h3bFZvC0ckfp5G9eQdJ7k9Fn6fFuo8DZ5woABpTEhY3lXKqejBwttmpwcHDAp3oA9boOqTJRQZIkceLECTZs2FBc4LBNmzZylV4Zmdsgr4TIVBqTa/rw+o4PaLkiHZXC9DUSMKX9dvPoh719HWzcbCArj4nxiYQ52NCwmvM95bbsFsTutAKczudSz6s2Z1Zt4J3OdTmHgase1mxLjOG5516k2vstqWlV+q+vUlG+SJO4M2kkLjxFoB5sgXEbv6JDUAs8Q5vA7l9JVEHOs7XJ2xEPSwVifexw8XXGT6HA2toahULBzp07S2WEKF1dUY7+nqKYIbBmPJp/B6Db0wPVs1+g9LwRsaHbuRTbUx+b3lz7O9eKYdi+vR5r25tq9WhuTdIGJgffNz/6lFatWtF0/s4SxwRAsXUrLOyA7TtbsP1Phd3SIuZkkfLWC2RGncbaS0ngDx9h1fYpxJ3fcGjTXxwmnCsnRFSIBDkoydFDYnIeexIvcvrMOzw+eCy2rvdO7FbRCIJAeHg4tWvXZteuXezatYsjR47IIb0yMmZCNkJkykUnN0ds8jahB5ycmyIBCcuOcOG3aLx7RPH4p5sRkWhnq2HDZ1Noum09VoJIt26mInh38pOwtlbT4aUGiEaRp61GMeXjN2j16uO0atmcmTNnYhB1vP3O61iV0gARry30Kco4WRj1Iknv7cIKuO6hsfDMZk4UJXDowCGsra35cIsTtetXIzTCCw+fbrz1jh2bE5fx8eiP0Wq1vP322xiNRpKSkso0tqZ+M6R62ylcPgvNic/g+yYUhr6BLisbqxPfITn6gRIkEYp6/I5V88cpy2bTqFGjOH78eIlqtCW4z4lVu+oXkqZ9jkEr4vlkY1wnz0WwMhlEitav0zjyaUIzEjlzcBs+IY3wDm9TfO6pf+aw4sBFvv/2S0a8MgJ775r3pYu50Gg0tG/fnvr167NhwwaWL1/OgQMH5JBeGZn7pGqseco8cGiUSiI9Iuke2J2tfX5mhu8IpB3peAV7oddnM9Zby8/hgbjM/xbb4/toOPQD/Ad/xvmLl+jXr9895SuUCt6eMoovvvicDz+YSv369YmJiWHdunV3dfT8L9c3G8tihBzZe5nDU29M0JetBdJ7u/LV7tksXrIYa2vrW87x8PBgyZIl/P3339jb2+Pk5ERWVhYNGza8oz/I3RCUSqz7v4706gGKnDtiffJ9nBJnYu1iwEYZb+qjAHHJ6DLJHT16NGvWrGHr1q13rUZbHozpiSQN7ULCxBmoHdXU/O173KYtLDZAinHwwrZGAxo8ObaEAQJQt+crvDrsOfIkW9av+N2s+pkDFxcXnn76aQYPHoxOp2Pu3LmsWrWKvLy8e58sIyNzC7IRIlNu8orysFXZkpeXx6BBg/jxxx9xdzXVBpl3Yh5ZWVn8/PPPzPzqS9Z99ir+tcMxtB7B7t272bu3dJVUR48ezcWLF9HpdOzbt49mzUpfAA1Meb6hdNsx2VkFrPtqHy4rz1OoFLjUqRo+01rRfGprLqScIyUlhYYNG6JSqVCpVGzbto1vvvkGlUqF0WikS5cuxMXFkZKSQlpaGr/99htXrlyhZs3y380rPX0o7DWdy7YfUyQGAJCSWQOtYw/ytZ4I3SaXSo4kSYwePZoVK1awZcuWUhWDKwvaxV9yoWsHsg8k4PV8e/zXHUTTsEO5ZDn7h9LIQ8+xq0Z2/vWtWfU0FzVr1uSVV16he/funDp1im+//ZY9e/ZgNBotrZqMzAOFvB0jUy6+OfQNcdlxdPDvwKhRo+jZsyedOnXC/SN3imyKWB+/nqO7j6LX62ndrjWudhp+f7kZz8yVOOvsyZpN22jevHmF63ljO+bu/Xavi8VuWyJBEpwLc+axgeGobirE17FjR44dO1binKFDh1KnTh0mTpxYIpeEu7vJENuyZQspKSn07t27TDpLkkTakfOk7jyDcNmIA86IUhjnVJPJT1/PvuQEnhvxHs51Q+8t7Br3KgYHkJycTHJyMrGxsYApPb+DgwP+/v53jAoxJsaSMmEoWdFp2AY44P/l92hC7z/xWM8RHxD94YdsOplO46IirDWa+5Zpbv5bpXfDhg1ER0fLIb0yMmVANkJkysWB5ANYK62xP2HPoUOHOHDgQPGxlj4tca3hyrrodQgqgS7/dMHX3pdmPs3435OP02emK79sOMSwV/Ko6VE5WSnv5EB4NTGXI/OPEZpj5KydErfBYXSs4XxLPwcHB8LDw0u02dnZ4ebmVtw+b9486tati4eHB3v27GHMmDGMGzeuREKwOyEaRZL3nCBjbzyaVCW2ggM2ooY8mxwKwkT8OjfE38WRnMtN2TduBH9PmcSgX/9EcZutodsxe/ZsANq1a1eifd68ebzwwgsA/PDDDyUqWrdt2/aWPjejXfQZiV/8jFgk4D28L85jpiGYKbFX7KlNtOQgu2nM5K++RnR0xsrFhcfqR9KpdlC5trgqiushvY0bN+bff/9l4cKF1K5dm65du+Lm5mZp9WRkqjSyESJTLqxUVvgZ/Zj01iQ2btxYwk/C2dqZL9t9SaMrjRg6Zyjvt3yfI6lHWHZuGcvPLUdjk45aJfLM3L0sHt68Qg2R66bHtH9O0STAhQ51PFEpFRiNItuWnsLncDq+QHwLL9r1qnVfk9uZM2eYNGkSGRkZBAQE8O677zJu3J2Luhl1eq5EHSHn0BWsM62xVthiLWrId9Siru+CX4cmqG1LGhmOftXp/OxQ1iyez7Z336L9F9+USrfSROJPnTqVqVOnlkoeQOLnv6B2taPanIWog0q/KnM3tHmZnFo5gcaxSyhwbUidpo9x6Fg89lcuIl69wu7Tx9no7MboZ5+hhqeHWcY0F97e3rzwwgvFIb3ff/+9HNIrI3MP5DwhMuXi1U2vcmDTAQ5+erDEVoTRaEQQBBQKBevXr6dTp07F6b9jUmL4YO8HrBm2Bp9uPni0bYNYEMifz7xFLS+Hu4xWfk4l5dB31i50BhEAjVLB6kGNSP7rDMGFcNJNTeMX6uFayuq+94suJ58rm2LIP56KXZ49aoUGrZiDzl2PSxN/fFtHoFTf+95g3cRxnDx/lv4vv0b1Lt0qQfOSiKkJnGnbBa9n2+D63o9mkRlzcAU+Gydga9ByqNlEWnV8DdW175beaKRAb+Cn3ftJ3bUdK6MezxZteLVrR7OMbW6KioqKQ3qtra3p3LkzERERckivzEONnLZdptI4mnqUZ5Y9gz5dj5XSiveav0eYe1gJP4nq1avj4eHBH3/8wZNPPgmYVgvq1KlDly+6YBVow/ncSxSmjKN5eCQftgjCw1ZDYp4OT2sNVioFiQU6AuysWXYhlW8vXCVHEot1SFn8C8nzvsW977NUGzEBQ042yb/NJu/QXopSk1E5ueDUoh3ez41Ar7HDUKBnRIbAkwlFZChA6lKDxu1qVPi/Vf7VTBI3HUF3Jht7nSNKQUWOlIHRR8CjdS08G4aUeQXGoCtk7rNPIABPv/8ZruH1Kkb521CwdQXxr04CSaDa6MdxHD3jvuSlZV0lbvlYmiWs5bBXKzz6fYef152deU8kp/DLX0twyEhFZ+eACOhtbHmqU0dCvT2LJ/qKeoZrK2yShABIogiShChJxa8lUUQSRbKys9m4dStx8fG4u7nxeNeuBNSufV//XjIyVRXZCJGpVI6lHuNk+kn+OvsXrtau/NjlR9q1a0f9+vWL66mMHDmStWvXMn/+fBwdHXnttdcoMOrhs0/JLdKSp6p+Q6AoXc94Znr9H29SZaER50IRECg8d4JLX7yDwtYOu/BG+Lz0BoUJcaT8MQeXjr2wql6TopQkkmZ/gnVAMNUnfUZAgcQP0QUAOL/bFHuHilsiz7l4laSNRxHPF2BvdEZCIkdIhxpWeLcPxb3O/UenpO7fx1+fTsVarWHoohXl3kravn07M2bMIDo6mqSkJFasWFGiaODVq1eZOHEiGzZsIDPlKo1trHnHw5vWU1/B/qmRCLblW8WSRJHdu38nZNt7qCUDp9tMoWmbF0uVLbVQb2DKrO/R5GRjCAxGGR+Hymgolx6VzSCjkcDx41G5uFhaFRkZsyJnTJWpVOp51KOeRz3SCtNYdGoR+fr8W/p89dVXKBQKnnzySXQ6HS06PMa5F8ajFF3xVqRjK16gt184vV29+Pb4ZRyUSnztrMgwGEjX6dlQkE81vcCLgZ4Mqe2NrVpFXl4eDSc+y9rfF/DRRx9RP8iLmX0aA43htadLjL8kxI3nnnuOmG6RKBQKok/vwUcrIqjM79iYcfEyiRuOwHkdv+1ex7qz24nNuIi1lRXNWzTny2++LuGk+sorr7Bp0yYSExOxt7enZcuWfPrpp9SpU7paKh5Nm1GoUlIoGcm7GI9jYPlCgbVaLZGRkQwbNqxEDhfdwa3oDm2m1ye/oFIILOzXjKKNR/g1P5fh2RmcfnIUgm35trEupSaQuPx1WiVt5bBfJ/z7fUMz12qlPt9areLTsa8Xv1/3x19Yzfkeg8YK92eeNq1GXDt2812WBDeOSf89duPd9Vsz6aYG6ZpNLJXYUhFMRrNwLReNIFxbMbn2fFO7BEiZmRh//Y24DRvxHDsG5wEDzObMKyPzICKvhMjcNwk5CTyx6gkGhAxgYtOJd+37WvQaluT40VAVz9o2fcs13pAhQ3B1deWrr766ZeXlv/z0009MmjSJ1NRUAP7aeJaWm6+SbK/E2NSLxh0CUKrubxK4vP4wuRsuYaO0RymoyNVk8cqqj3ju5SG07tAOg8HAO++8w/Hjxzl58mRx1dq5c+dSp04d/P39ycjIYOrUqcTExHDhwoVSl49fP20Kx49GU9/Lnw5fz7pvnwNBEFj2+0Ja7lxG5paTxBfp6XHhPKsCAql1zbnSa1R/6nz4Ax9//DEvvfRSmeQbRZHtUXNpsPsjjAoVSR0/IbzZwPvS2Wg0sqNDJ2xyc3H5bSG1w6pWQbz/YkhPJ+XLL8lethyr0Lp4v/cetg0aWFotGZn7plzzt1TFyM7OlgApOzvb0qrIlIHvD38v1ZtfT9p9Zfdd+z239x/Ja8th6XjGlXKN88cff0jh4eFSQUGBJEmS9Nhjj0ljxoy5bd/U1FTJ399feuedd4rb9HqjtHbWAenSxO3SpYnbpR274sulx81cl3Vp4napIPn239uUlBQJkLZt23ZHOUeOHJEAKTY2tkzjb3xvovT5gJ7StvfeLtN5twOQZgX5SadCQ6S0twdKh7ZslADp7LFjkqjTSaIoSpIkSX5+ftKQIUPKJPtC0jnpwHddJWmKo3R43iApLzvlvvWVJEnKzdNKJ0PqSCdD6kg52TlmkVkZ5B8+LJ1/op90MqSOdOXtSZI+Lc3SKsnI3Bflmb+rTrC9zAPNK5Gv4Ofgx7zj8+7aL7nQlN46sfDWrZt7cenSJcaMGcPvv/9+29TpN5OTk0PPnj0JDQ0tEXaqUino/mpjjjUx5W8ICL7/PA6qfu7Fr629bm/9Z2dnA9wx6ZdWq2XevHkEBgZSvXr12/a5Ex3f/4QwnxocOHOM6G++LNO51zEkXeLKoM4AqO0V1PztO9w+WUR468fw9/fn3Q8+IEurRa/X8+mnn3L58uUy1cQ5c3Y3AT80onHqHs70+ZX6LyzEztE8Ibb2djfSwl88GE3yhXiMRUVmkV2R2NSvT8CSv/CeOpW8LVuI69adjN8WIhkeDN8WGRlzIBshMmZBISio4VgDjfLumS2f9w8yPZ/OI7WwbPU2oqOj75k6HSA3N5du3brh4ODAihUrUKvVJeRIkoRXmMlwOLbrUpl0+C9J+06SuewcAGkuKbftI4oiY8eOpVWrVrckPPv++++xt7fH3t6ef//9l40bN6IpY3ZQQRDo8sU3BNo7s23nZk7+/mupz5Ukiay5n3C+Wxe0xxIAcH/vCzQNTVV/1Wo1y5cv5+zZs7i6umJra8vWrVvp3r17mRxhQxZ1B2B/z3mENOhThqsrHRe69gRA+epIMrt3Z2vvJ0hNSTP7OOZGUCpxeeZpaq77F8fu3bn68cdcePIp8g8etLRqMjKVgmyEyJiNuKw4gpyD7tqng1dNNGI2igwdjRbsKU6iNX36dARBYOzYscV9X3nlFYKCgrCxscHDw4Mff/yRVatWERMTU/xo3LgxgwYNIiYmBqVSSU5ODl26dEGj0bB69epbVkyMksTaH6LxnH8GAJfq5YvsKMrVcuzzVeiXpwICqn7u1J/45G37Xq9au3jx4luODRo0iMOHD7Nt2zZq167NgAEDKCwsLLM+CqWS3l99j5sosG7Vn2RfS71+12s4d5xLvVqR9OWv2AXbU3P5HwAIypL+6o0aNSImJoasrCySkpJYt24d6enpZaqJc9Xak2SNO00aP1G2CyslPb7+HLetWzk6agzHh76MV8JF9o56DYNeXyHjmRuViws+H7xPwF9/IVhZcfG557ky4S30Kbc3bGVkHhZkI0TGLCTlJZGkTaKWS6279vOzdeFih7bYn7mM4kIRDb7Zxs//bGbOnDlERESU6NuoUSPmzZvHqVOnWL9+PUqlktGjR1O3bl3Cw8MJDw8vkTr9ugGi1Wr5+eefycnJKa6Hcn2VRC+KRF40hemqxtaneeOyV5K9tDGa+A+34ZjqSJ5/PrU+6Ip307q37XuvqrVOTk7UqlWLtm3bsnTpUk6fPs2KFSvKrBOAytGRNkNeRhIEtn72IbmXEm7bTzIYSP/4Dc73fYqipHSqT3iKakv3owpueFf5Tk5OeHh4cO7cOQ4ePEifPqVf0Uiz9uSqXbUKTdbl6ePN06+NoP/E8WRM+4TgYzFsfH9ahY1XEdjUCydg8R/4fPQh2p07Od+9B+nz5iM9IMaUjExZkUN0ZcxCTGoMAI08G932uM5gYPv5OHT6IgySERshlyKsybqayYgPRtPujalk/VNyG2H48OHFrwMCAvjoo4+IjIwkPj6eoKBbV1wOHTrEvn37AG4pIHbhwgUCAgKwUijIsFHgWiByIT4Tb+/Sr4QUpGZxfs52nPJcKFLqcH2uLjXCbp/vQ5IkXnvtNVasWEFUVFSpqtZKkoQkSeh0ulLr9F8CH+9Nw/27OXL6GD+NH4mH2gr/oNo0Gvk6dr6+FB7cSdKEMRQma3Ft6onHp/PIt/ci5siRYhkXLlwgJiYGV1dX/P39WbJkCR4eHvj7+3Ps2DHGjBlD37596dKlS+mUkiRcC1NJtPEt93WVlczYODwB+/qRlTamuRAUCpyfegqHzp1J/fobUmbMIGvZUrz/9x52zctWRVpGpqojGyEyZqGJdxPUCjXr49fzQvgLAFzKSmfHhVjydEX8vOssV1O9bzrDA3uHVOqe2ER+vZacy/El+VIWaS4pfCVJt9wx38lxMyoqqvh1u3bt7lkjRRAE3IaFc+KvUwSvukhGLXecna2RRAml2hQWKxlFBOWNRUJJkri4fC/ivjyssSY3tIDQ53ujuEsY7b2q1p4/f54///yTLl264OHhweXLl5k+fTo2Njb06NHjrtdwr+tr/+GnNLkYz+H5P5Nw5iTRp49x4rUXaaOQUB6Px8oZAj55FZs+r4EgcDAqivbt2xfLGD9+PGAKhZ4/fz5JSUmMHz+eq1ev4uPjw+DBg3nvvfdKrVNqShw+hVc56X7/1XVLTZopJFuhenB/4pROTnhPfg/n/k+R/MGHJLzwAo49e+I58S3Unp6WVk9GxizIeUJkzMbE7RNZF7+ONtXa8EGrD+j23SpS0m78WCpVWj4fGIhaoUKlUBK3cx8/zPyGAwcOEJWcR98OHdG4BhD08psceKkVKqWC77//nrfeegutVktISAj//PPPbVdBysr55Fw0M2OIc1XjmmdALUokBDugSM6nTpaR2M6+1G9aDWVaNlfmHcS+yJFUdSKBL7XBuca97+jvtO1wvSJtYmIiL730EtHR0WRmZuLl5UXbtm2ZPHlyqaru3oyxSEdOwmmunjqEPicVirTo8/MQi/LJy8njQnwmGflgX6DjyZoa3N6fh+BUcZOYJIoc3r8Ytb0XeqWGukueRCUZOPfcBkKDGlfYuDeTeDWV7Mfacqp5K/rN/6lSxqxIJFEke+UqUj7/HEmnw+P113AZNAjhATayZB4+5LTtMhYltyiXhacWMufIHKo7VKcgqS9xV+xZ8GJ9PO0dCHB1x1plivy4dOkSjRs3ZuPGjcW+II891o5DRlfcWr+I2lFNx4bVeDXMDZvCXJKSkvj888+5cuVKcVGw+0FnMJI4eTdqERLtFGT72eF3Pg8HvUSBEmxMLiRIkoTWmIPQwpba/dpXiQJkkiSReS6G8+t+JSb6FNmFt05ESkFEqQBbtYSLkwZHZxdCWnWnetenbyOx/Oz4+0PUmRewz7lIkaBGqtkW11PLCcw5V9zHICi5MGwHtaqHmXXse7GxYxf8rlwi7on+FGrUeDRuzGO9uleqDubGmJ1N6tdfk/nHYqxq1cJ7ymRsG91+C1RGprKRjRCZKsHmhM2M3ToWo86T/PNjaFArixUvDi7RZ+XKlTzxxBO3rcArKBTU/2IDaUkFIICdizWiQWRAM18+792Un3/6iYED7y/LJkB8Ug6HohNp3NgXf+8b3zVRFNl1LBlWX+CoJpMhw1pg73H/+UTKgySK5MYdIulQFBcO7ePi5SwMkpJCowqlIBLorSEwIhJHr2p4htTDyjMIvWCNtUPF/+0kplzA9/v6t7TrBSV7G4zByrM2Ym4KYS0G42Bf+XVSTh07wdkpH+CYehWnnGxsdIXo1WriOnUjfOhgakWE31tIFaXg+AmSP/iAwqNHcerbF88330Dl7n7vE2VkKhDZCJGpMtRbYKrqmn/xRYz5tfh3TBvq+tz4PHNzc7l48WKJc26uwBseHs6JlBzmxFzmn4OXMebokQx6Ln39DAEDx/P9tDfoVr3iDIPR67aw1MqVXinn+fHpfvc+wcxIRiPHFn3OrvVR5OtNhpqNykhITResbazwCapF9S5DUbv4VLpuAIVGkcycVHy+NlWEPRY+jDznmvjE/ERety8ID+9kEb3uhNFoZPUPP2Oz7l8cUlNwzcogIbg2Xh9NIzIyrEqscJUVSRTJWrqU1C++RBJFPMaOweWZZ+RaNDIWQzZCZKoMeqOeIrGInAKRF+cfJjW3iIUvNiPkLtEoN9eB+a/j5qm4eCZ98BEno/fjMXw2CrUj3v6OtA/x4IO2tdGYuSDd9KidzJTsAdge6kttr8pzBLx6cg9R333C5XSROr4Ctdt2xbtJVxz87h7+XBkYigrILiwge04HXHWpOBvyOOjTjoihf6DR2N5bQBUgNyeXzQv/xP33BbilmxKaXRw+km7jX7/HmVUTQ2YmqV9+RdaSJViFhODyzNO4mGGlUEamrMhGiEyVJC1Px3M/7SM+XcsPzzWiXcjtJ/SbjZC7OW5ae1fnf5vPsmP/FQAEGyV/v9aacFd7s+k88O+NbLU3pRU/2jAQTycns8m+E3qDgbl/LMLwzx/YqYro/OwAanQfbqrSWgVIy7iC1azGOBhNKfd313wSR2cfAtuMxM6l7PlWLE1hQQE7lq3C76P3AfDevx8Xx/Ilr6sKFBw5QvzTzwBg27w51b74HJWbZbYRZR5NZCNEpspSUGTkpV8PsCs2nQ51PHm+eQ3a17m/1QVRlHjqr2gOxVwF4NgHXXHQ3DtaICe7EFtrFSqrO/f13hoDwD9B7jTyr/gJ9nxSMivGmirSNnBNpI3HBVLtvHF+fR+2Npb9O7h8NY7E+c+zaEU0K07rSdFKBFd35qc/1tGs2YOftyLqr2V4Tf4fZ7r1pO/Mzy2tzn0hFRWR+Pbb5O3aDaKIx+uv4zLwGTmKRqZSKM/8LWdMlakUbDRKFr7YjGlPhLPldApD5x/g9T8OE3Mpq0Q/SZLILTRlhzx2OZupq0/wy84LXMkquEWmQiHQtOaNO73wjzex6EzybXOFGIwi7204hffWGGofOk2njcdYFX2J7XGp7LiQxs6L6exJSGfflUz2J97Q6Wp22erblJf4RNOqTmZYIxrM2My+wJ74FiRy4o/h9zizYjmyZyEuc1sx7fdjrEp04n8fv8vh46d5cvAYunTpwpUrVyyqnzlo+YQp82vIun/Q30eiuKqAoNFQ7csvCbq5Fs1T/ck/dNjSqsnI3BZ5JUSm0sku0DN28WHOJOeSmF1ITXc7ans5YKtRsu9CBleyCnC315CWV4SngxVZ+XoQ4O1udRjaKqCEE6EoiqyLT2fWzvOcOGna39fYqHimXU3ebVUTK5US0SgydPtp1lNEV53p/RWFxMlSRPl6FYgc6lQPpUZ9785lZOfhQ2xa8BPKnEzURTrU+iJch45maLduAGR9XAPnoixOPrWMUAs4ehpFI8oPXCnQSzh8ms+qVavo2bNn8fFGjRrRvXt3Pvroo0rXzdxE/TgPry8+I755K7o/BHlFrlNw7DjJ779P4fHjOD3ZD8833kB1h0rOMjL3i7wdI/NAYRQltpxOYee5VGJT89AbJLydrHG3t8JWoyTQ3Y4+9X0pNIh8ueEsv+y6wGsdgnmjy41kXicvnWHD8ROsjNEiCWouZpXc03fysaO2jz07PFT0NqiZ29mUq0KUJM5fycZQaESSJERRRBJNWzyiJJF9KQX9oVw8dRKqAC0hgzuiNNOSdlZuLnPnzka5fzsZPv64hjUAAZQqFc/1ewr3a34JRTotmk9MidEuDtlCjcDKyweRlp1Czs89qZlzlm3V+tBu+G9s2rSJjh07Fvdp3bo1KpWqRNbaBxVJFFn91jsE/7Oaoh9+pMFjrSytktmQjEayliwh5auZAHiOG4fzgP4IZaiCLCNTGmQjROahZtbWWGasP8Oc5xvhY5fMH3ti+OuoC0bpzsaBpBRQtPPhDUdnRje7d/2W6+gLdcR9uBFrgy0qQU2OTw6hY3re+8R78PeWLRxZ+BPKokI0PZ5i1ID+WKvuvMqSMCMCf+1FDnT6miatX7jv8UtDnsHI6a9b0zj3JLsbjqPF45Np1bo1Go2GRYsW4eXlxR9//MGQIUMIDg7mzJkzlaJXRaMrKuJk46YU2NnTfNd2FA/ZJG3IyCDl8y/IXr4c63r18J4yBZvwyk0gJ/NwIxshMg81kiQxcuEh1p1ILm57MuwyH/R/Do3KiqTMRM7HxvL7Kj29vA1crebPQW0Bz0saWg+tX+bxZs+ezezZszl/NhZBEqhZrTrTZ31F9+4ls25KkkSPHj1Yt24dK1asoG/fvrfISkpN4ZdZ32B9Kob04DCeGfEaEdXv7fC6888xtD413zTOlCyz57NITrtEWvIZMi/soUgCq4IMrPKv0uTiP6z0f5K+w34BIC4ujmHDhrF9+3aUSiUNGzakdu3aREdHc+rUKbPqZEk29OhN9fPnCDp6FE0FbMFVBfIPHSL5/Q/QnT2Ly8Bn8BgzBmUlRH/JPPyUZ/6WXaZlHhgEQWBG/whiLhwhU+fE4hcDaVDzxuqEv4c//h7+1Ij9A/UJP/SFZ+jbywOPsKblGs/Pz4/p06cTVLMmZ2ZtZGP0AXr36sX635fSfkCfYoNg5syZdzQOJFFk8aoVxC9fhKhU4zD4VcZ274aylHfZzZ+YDteMkIzpQVxqMYF6bV5CqSzdBCmJRuLP7URbpMPF0Z2EvDzy9vyAX248hVZONEjZjfdtztsb/jKP951e/D4oKIht27ah1WrJycnBx8eHp59+mpo1a5ZKjweBqL+WU/38OYpUKlSKqhEWXRHYNmxI4LKlZP7+O6nffEvOuvV4TpiAU98+D2TSNpkHG3klROaBIyVtBwcODadO8MsE1Rx/y3FRFLm6fxOFUXmosl3RhcVSvV9frGzLn9a6KC+f2G+30PKDZ/lfu5F0D2+OwRdSfCSGvvk6Bw8exMfHp8RKyPn4C/wxaybWCXGk1m/BK8NHEuhWDqdASeLQ9h9puHWC6fo1rpzu8g0tIjqj1tzwrpWMBtKzk8m4sI/cs5txSzmMT3YsVmJRCXGFCg3nHWtTZO1ChlNN3Or1JaRWC4ySEQUC6VmJ+HkF3zU/SWZmJoGBgXz22WcMH27ZCB5zsez1NwjdsJb4F4fTfcI4S6tTKeivppDy6afkrF2LTaNGeE+ejHVIbUurJfOAIq+EyDwSuLu2wNHWGaNBe9vjCoUCn+ZdEBsbSfp7A1b7g7lycQvWXeywdffHsUYoCkXZUlsrbaw4GphHgaijQZ/GCGkqNJeUjJr2Km+1eZ6rf+4FTFszBr2ehb//Rsr6VegcnKk+ehLjW7cs/12mINDwseHoWwzmyI99aJy6F881z8Ea0+GrNt5ccAmneeIm3AF3IF9hxSGvNsQF9cK1ekPUNk4U5KVha9QR2rAPoXdZSfHzvjUz6/r165EkiZCQEGJjY5kwYQJ16tRh6NCh5bumKkizMaPJ3bAWZU6OpVWpNNRenlT78guc+z9F8gcfcqFfP1xfGILHqFEobB+MDLgyDzayESLzwJGVtZ+iohS8vfvctZ9CpaTaE93RNrxE6uIcxGWO5JFFpt9Saox8EoXy3l//Y8eO0aJFCwoLC7G3t2fFihV07NEDgJdffImGDSPp1KAldommbK0JC/by18ZDpGQeIaNFZ8YMHYaPg939XzSg1lgT/Nxv7N/3G9VjfsYn35Sjw6sgGa8Ck5/MUd92aO18iOw/k9aa+6s0fDPZ2dlMmjSJy5cv4+rqypNPPsm0adNQqyvebyIuK58f9l+kQBQBUAimrTkBAYUACKaERwpBMLULIFx7f70v194fTMhApVSguNZJcb2/ICBJIvWD2qI5mEBE/BW8A6pV+LXdi+3btzNjxgyio6NJSkq6xedo6tSpLF68mEuXLqHRaGjUqBHTpk0rcxI5uxYtqLlqJem/zCNt9mxy/v0X73ffxeGmaCgZmYpA3o6ReeC4fHkhZ899SPt2pxCE0vlWSEaRjCMHKfjLlIxKrJ2F/7Be9zyvqKiIhIQEsrOzWbp0KT/99BPbtm0jNjaWN954g8OHD2Nvb49eq0Njb80XT73NgKAeHHIVaTKsMT7u5jFA7kRm2kVUNk442DmDJFWZFO/mpN+yQxw6kIQkANd+re73KhU2SpMsybR6VSzXYECUFPjrMtk47SmsbM1nyJWHf//9l127dtGoUSP69et3ixGyaNEiPD09qVmzJgUFBXz11VcsWbKE2NhYPDw8yjVm0aVLJH/4IdrtO7Dv0AHvd99BXc3yBplM1UeOjpF5JDh1+l2ys6Np3mxdmc7LS4wj65tEABS98vBq2gGl2qpMMjp16kRQUBA2NjZ88803JcI4jUYjCoWCiIgmLOj9ORqjxJXHfGjfseZDF+5ZmXT6dR8JCdmcebfzLYnqRAlETPldjJhyvIjitTYJjKKIdO21iIQoSlSzt7rj57F/014GbkjBqFCyc0RD/AIsU6X4dgiCcMfoq+tc//38b06XsiJJErkbNnJ12jSMubl4jB6F6+DBCJWw8iXz4CL7hMg89Oj12aSk/IOv79NlPtfeN4hMx4sIOWrEv+25tH4zQrM81K72uNRtiI3z7eJESiKKIjqdjvfff5+XXnqpxLF69erx1Vdf0atXL5xcvdn/1ynCNyex9Xg6wU/XoYbvox0GufBkIotjTFtICkFAobi+ZXLtNQJKhWkLRSHceB17Kg07V+tbfGoUCsWNuhNmql5/NUOL8Zq/0JXYi1XKCLkXRUVFzJ07FycnJyIjI+9LliAIOHbtgl2rVqR9+w0pX3xJ9spVeL8/FduGDc2ksYyMbITIVGGKitI4eWoiVlZe1Awci0bjzpkzk5EkCf/qw8ols/o7HdAmXyT7zCmkPTpUO3wQgfRV57AaEIdHwxuZMidNmkT37t3x9/cnNzeXRYsWERUVxfr16/H29sbb+1ajxd/fn8BAU1K0bi83ZH/0ZVzWXET/3VHWN/egY8/aqJSP5qrI19viSLmUg9pGhSQBkkTxTsi1F8XrsjdtkSCBv7f5KiTfjV4DOqLTrePNE0ae25DC6bb6CknZb07WrFnDM888Q35+Pj4+PmzcuBF39/JHgt2M0t4Or0mTcOrTh6Sp73Px2UE4PfWkKf27i4tZxpB5tJGNEJkqiSjq2bO3EwZDLgDJySuxsamBVnuO8LCvsbLyKrdsO+8a2HnXQGwjotdmok/NIWvuZXR/iWS7nMAp0JRFMiUlhcGDB5OUlISTkxMRERGsX7+ezp07l3qspo38yKvjyY5lpwjbncruU5l49a9NSM1Hr8S6wSji7m1P9OuPWVqVu/LU89048tlSfsuwof/4X5gysBmRrepbWq070r59e2JiYkhLS+PHH39kwIAB7Nu3D0/P+6tSfTPWoaEE/LHIlP79iy/J27QZz7fewumJvnJuEZn7QvYJkamSHDv+Gikpa6lT52Pc3Tty8eIP5OaeJCDgVdxcW5t9vItLl6E86In1QA3ukRVTnv7IiasUrIzDO8/IqQYutO9bB2vNw3MfIEkSMw7Ec+hipslx9HoUiwACAjv3X8HLz4F9o9taWtV7IkkS70yezx96T2oUZbHty0GWVqlUPiEAtWrVYtiwYUyaNKlC9DCkpXH1s8/IWf03No0b4TNlCla1bg3rlnn0kH1CZB4aJFEPwOnT79CwwSJq1/pfhY5nuFyAEihKzaywMSLDvCis5cbWlaepeziTmHP7sekbRGRY+Vd1qgqHUnJ48a8YMi/ngpXyRviKdO1/EqAUCPF9MG4sBEGgRk1fOGNAh4LD63bQoFsbS6tVKq77LVUUKnd3qn32Gc79+pE89X3OP9EPt6FDcX91JAobmwobV+bhRF4JkamSiKKBPXs7UFh4BR/vJwkN/axCx8uOO0H6nyfR5HijGSDhXDcclZVTmZOalZYz59NJWXKWGpkGToQ60ubJutjbaSpkrIpEbxQZs/EUa3deRFAIDO9am7db1nwolugNegOffrGEnzPsEBVKdg+ujW9o5d7x5+XlERsbC0CDBg348ssvad++Pa6urri5uTFt2jR69+6Nj48PaWlpzJo1i0WLFhEdHU1YWMUXpxOLikj/6SfSf5iDysMDr/f+h0O7dhU+rkzVRA7RlXmo2LwlGJCIjPgJd/f2FT5ewufrUKTdyOuhc7mE3ytdsXKuGP8Ng1Fk8z9nCdqbSo6VgqzOfnRoWaNCxqoINh0/x0sLzwJgo4GNb3bAz/HhuxPe9e8uBm3L4lVNIm998HKljh0VFUX79rd+94cMGcIPP/zAs88+y759+0hLS8PNzY0mTZrwv//9jyZNmlSqnkUXL5L8wYdod+3CoXMnvN55B7XPgxNZJGMeyjN/P5pu+jIPBIGBYwBQqRwqRP4nn3xCkyZNcHBwwNPTk5HrZnDO8wiKxwow1kvFKrM6qdNPsnXNKjp06ICdnR2Ojo60bduWgoKC+x5fpVTQtXcd1KMjMApQe3UCBYUGM1xZxZKdX8BnP3zP8i++Lm5TFeRVqAEyderUa/4lNx516tQpPv7KK68U52/x8PCgT58+nD592ixjX0nJBmBJrj0FOblmkVla2rVrhyRJtzzmz5+PtbU1y5cv58qVK+h0OhITE1m1alWlGyAAmho1qP7Tj1T76ksKYo4Q1/Nx0ufNRzJU/e+zjGWRjRCZKksN/+GAwJmzUytE/rZt2xg1ahR79+5l48aNYGXNC1/PwKltK/yffQKhaw77rx6gz1PP0iqkDvv27ePAgQOMHj3arMnHavg6oW3rC4AkVqmFSQB0hQVsPhzDgrVrmfXHImaOGQ7b1hPYPJzjUzrwhE9hpWy/hIWFkZSUVPzYuXNn8bFGjRoxb948Tp06VVznpkuXLhiNxvse94lnuzDeNYtUK0e6vbuMxb+uY9kvqzEU6e9b9sOEIAg4du9OzbX/4NyvHykzZnDhqf4UxMRYWjWZKoy8HSNTpYmL+5z4i7Np2SIKG5vqFTpWamoqnp6ebNu2jbZtTREcTRo3oKl7NSbVn4jBPwWfZztg5Wz+/Ahbt12g1r+XcZ3cDFvbivcNyS0oYO3Onej1RUhGEUkUEUXjtWcRyShSkJdDzq4tWOfnlTg3u2Yd+r88ino1TflQxs5cxspka95uZI2jrRX9uzZHrTKvz/vUqVNZuXIlMaWc0I4ePUpkZCSxsbEEBQWZRYfnJy5gh3Aj/0YHQxK/fP7SXc54tCk4foLkqVMpPHEC5/798Rw/DqWzs6XVkqlA5OgYmYcOL+8+xF+cTWzcZ9QL/7ZCx8rONi27u7q6AqY8IQejY3j+6xfoO+cl4i+mEPRpdd6d+DK9Ro5+oJ0vl23aRPrCObe0i4KAJCgQFQokQcCgsUboPZCgGgG421iRXaijW4sWJVaCfF1sIVlkenQhUIi32yk6NK9ndp3PnTuHr68v1tbWtGjRgk8++QR/f/9b+mm1WubNm0dgYCDVq5vPcP3t0yGkZ+Yy68s/+UXvQwyPdgbce2ETHkbAn4vJXLyY1K9mkrtpE14T38Kxd+8H+m9HxrzIRohMlcbezhSNkJKyFqg4I0QURcaOHUurVq0IDw8H4Pz58wC8//4HfP7554TU8OTHj76h/+tv8G+KgZavD8La1TwJoa7/KIuVtDBZVGQK4Rz2y18oFQoElQKloLxRVRZTbg/ltRTqd+OtId0ZkZfP/pjTvLQmCb1BNLu+zZo1Y/78+YSEhJCUlMT7779PmzZtOH78OA4OJp+h77//nrfeegutVktISAgbN25EozHvqpKbiwP9ujbklzVJZKhsGTRmLl9N6IOn34MfZl0RCEolroMG4dC5MynTPyVx4ttkLV+B95TJWNWsaWn1ZKoAsk+ITJXHyqrivexHjRrF8ePHWbx4cXGbeK10/CuvvMLQoUNp2aEnv2xeR1CNaizduIerX0WTfmJ/CTlGg54ibdYdxzHqb/gRGEWRfWdSWBUVR9FuU2E9sZLuEM+eOMnPOw4QVisYJ1sbNv+zFluVEmulAiuFguHDhqFRKkwGyjVH0G7dut1RnqO9LXbWpvTmCjNvxQB0796d/v37ExERQdeuXVm7di1ZWVn89ddfxX0GDRrE4cOH2bZtG7Vr12bAgAEUFhaaXZfw1g2J/agb9XUp7LKpxq8LN5p9jIcNtacn1b78guo//YQ+KYnzffqS8vXXiBXw+cg8WMgrITJVHi/PHiRc+pnkq3/j5fm42ZdyR48ezZo1a9i+fTt+fn7F7T7XQgxDQ0OL2wRBoF7jpmSIeiSVnpx9sThUr0Pa3t3Y1woke24SSAJF1RJQ1ACPBi2xr27ySUg5uJ2ipSbdlT0KOHrchbCEQq4XST/jZ00Hm8qpU1JYWIivsyPTpn1Gv379btunW7duzJs3r/i9ldXdKw4XR0IoKya3ys04OztTu3bt4hwaAE5OTjg5OVGrVi2aN2+Oi4sLK1asYODAgWYfXxAEjqtN23aPtY0wu/yHFfvWraj592rS58wl/ccfyVnzD96TJ2PfxvxZkGUeDGQjRKbK4+XVC21+LCdOjOXy5YVERsxFrb7//XhJknjttddYsWIFUVFRxYXnrhMQEICvry9nzpwp0X727Fm6d+8ORhXqs9VJ+fgIYEfOlhQElBg80lGmu6K64kjW7kSym5/Bp1dXXMIakrzsIIKkxLjWhjAK0QsS1d5pjtpejV8l7pOHNmxIYNxRnnjiiTv2sbKyum2Rvv/y7/bDzN58Cq1eApwrLMHbzeTl5REXF8fzzz9/2+PXQ1krLHNoYQEGhQorYxFNWspGSFlQWFnh8fprOD7+OMkffMCll1/GsUd3PCe+jdrLfPVuZB4M5O0YmSqPo2M9IiN+JqLebLKzD7J9R0MuJvyIrijtvuSOGjWKhQsXsmjRIhwcHEhOTiY5Obk4B4ggCEyYMIFvvvmGpUuXEhsby3vvvcfp06d58cUXUTTML5YltkpB6pSK7RB7At7oS8DUnni+a3LOlPY6kPjubrIvnsB7SiMM9qbU8IUOF0mJ/JZ0RValO+pd99q4W3BcVFQUnp6ehISEMHLkSNLT02/b7+/95ziqc8JKEGlsnUX9WtVu2+9+ePPNN9m2bRvx8fHs3r2bJ554AqVSycCBAzl//jyffPIJ0dHRJCQksHv3bvr374+NjQ09evQwuy4ASjs7mhrScNdrMeiKKmSMhx2rmoH4z/sF3xmfod23n/M9e5Kx8HckM4RVyzw4yCshMg8EgiDg4dGFOiEfEXf+C2JjpxMX9znubu3x8e2Pm+tjKBRl+zrPnj0bMCWEupl58+bxwgsvADB27FgKCwsZN24cGRkZREZGsnHjRlPYZ1AQYm/DHcfVODjjO60Vcau/x2ZfJIXzi9CGbkT01cJZF+IaH0OlPsTufT0IDJtLQ6+GZf53KS/FpscdjJBu3brRr18/AgMDiYuL45133qF79+7s2bMH5W22W1zFPNZOv/2qhDm4fPkyAwcOJD09HQ8PD1q3bs3evXvx8PBAr9ezY8cOZs6cSWZmJl5eXrRt25bdu3ebtZLsf+le1533z8GPv25i5MsVY+w87AiCgFOvXti3bUvKV19xddo0sleuxOeD97G+aRtU5uFFzhMi88AhSRJFRamkpK4nKWkJubknsLcLoV692djaVs2054UZKaR+eRzBYPL5kAQDLqMD+Dvxezxzl3Pepicvt/im0vT5YsF8WLuU8X+sRqFU3rM66/nz5wkKCmLTpk107NixxLFXP/+LvSkShz57umKVrmLk5+TRb8oyTlu585JzLu9OfFoOPb1PCmJiSJo8BV1cHK6DB+Px2mgUtraWVkumlMhp22UeCQRBwMrKk+p+z9O0yWoaN1qGwZjPnr0dOHFiPKJY9ZbHrV098fuwPW5vBuM40gO3sSGsy/gbz9zl5GHPgAYfVooe6VlZfDJjOqxdSlZgHSjlpFmzZk3c3d1LOILeTJW6k6kkbB3t+fvjp+lLMj9lObB7y0FLq/TAY1O/PoHLluIxdgyZixZx/vFe5G3bZmm1ZCoQ2QiReeBxcqpPs6b/4OTYgOSrq9i+owkGQ+XW+CgNgiBg4+6DY4062Hr5or82cxtd+uJkXbGJryRJYvHatcweOwLp6EGUTzzP/z6aXuo798uXL5Oenl4cMXQza9PsyFTYm1vlBwK1jTU6TP+GLr6yU6U5ENRq3F9+mZp/r0YTEMClV0Zwedw4DKmpllZNpgIokxEye/ZsIiIicHR0xNHRkRYtWvDvv/+W6LNnz54KKfYlI3M3VCo7GjdeSkDAaIzGPI4cefmuTpdVgV51hgLglLmQBVu78su+CegM5s+bcPpiAu+/M4ErC76nIKAW/T77jpce78mp48eL06BfuHCBmJgYEhISyMvLY8KECezdu5f4+Hg2b95Mnz59CA4OpmvXrmbX70HHRmUyQoJqyAnLzInG35/qP/+E74wZ5O/bT1yPnmQu/hNJNH8yPBnLUSYjxM/Pj+nTpxMdHc3Bgwfp0KEDffr04cSJE4DJAOnWrRtdunRh//79FVLsS0bmbgTVHEdEvTlkZR/g1KmJGI0VFKJpBuw1DniELSFZEYyfFEsN7XL+Ovb1vU8sJboiPd/Mn8eqSa8jpCbjM+INPpryAXV8vDl48CANGjSgQYMGAIwfP54GDRowefJklEolR48epXfv3tSuXZsXX3yRRo0asWPHjtvmClFJRtq6PLo3Gn0bmfyQpsxcZWFNHj5MjquPE7T2Hxy6diF56lQuDnoO3blzllZNxkzct2Oqq6srM2bM4MUXX6R58+Z07tyZDz8s//627Jgqc79IksTx46+Rkvovnp49qRdeeQ6f5SVPX8DKvYPw0R/hvCICd9/n6RPct9wG/OZDh9jx8/fYpaega9mRkcNews3ezsxam6j/1p+09lbw3fj+FSL/QeCpUbM5a+VGzOdPyTddFUj+gQMkTZ5C0eXLuL04DPcRI1BYW1taLZlrVKpjqtFoZPHixWi1Wlq0aEFKSgr79u3D09OTli1b4uXlxWOPPVai3LaMTGUgCAL16n0HQErKPxw4+CQnTr5Jbu4pC2t2Z+zVNjzbagm66p9RUzyK4+UJbI2qxYKD75NvKL2jbXJWFh9+/ikxn05GVKlp/r/p/O/1MRVmgABICCge8aCQ7mFe5Gjs+G7uv/fuLFNubJs0IXDVStxfeYWMn3/hfJ8+aPfssbRaMvdBmY2QY8eOYW9vj5WVFSNGjGDFihWEhoYWF/uaOnUqL7/8MuvWraNhw4Z07NiRc3dZOtPpdOTk5JR4yMiYgzat9+Pr+zQ5OTEkJ69g/4HepKVttbRad0ShUNKj1pM0aX2C865vkqwIwi/nV5Zv78Cas3+xP/kQObq8254riiLz/13Hj+NGoorZh03fQbz35be0Ca+cXAsKHm0rZMiLj1M7P4Uv42HhXHlbpiJRaDR4jB5F4KqVqD29SBg6jMSJb2PIyLC0ajLloMzbMUVFRSQkJJCdnc3SpUv56aef2LZtG1lZWbRq1YpJkybx8ccfF/ePiIigZ8+efPLJJ7eVN3XqVN5///1b2uXtGBlzIop6Dh9+Hr0hm2ZN1z4w+Ry2XdxCfPw3+BmPlWi38n+P1sEvALDn8GF2T38PgLyQCJ4f+Ro1bxPFUlFEvLWEjr4Kvhr7ZKWNWRU5f+4SHX4+ioshn8OfP7pbU5WJJElkL1/O1c9mIAgCnm+9hdMTfR+Yv++HjUrZjtFoNAQHB9OoUSM++eQTIiMj+frrr29b7Augbt26JCQk3FHepEmTyM7OLn5cunSprCrJyNwThUKNj8+TaLVnOX/+S0urU2oeq9GBga2XY1XrF2xr/0Ker8nY0CV8yMYLm/l0wYJiA6TmS2OY8sHHlWqAgClHiPybDzYqBbYGHZkqW9as2G5pdR4JBEHA+cknCVr7D3Zt2pD0zjskvDAU3YULllZNppTctweVKIrodLq7FvuqUePOWSytrKyKQ36vP2RkKgJvb1OxtviL31tYk7KhUSpoXf0xWvg9Rp86L9C4xX502CCcH06o1WfU7JFAt7cG80TnzhbRT4JH3icEwCewGtvfMX0G26LjLKzNo4XKzY1qMz6j+s8/oU9M5EKfvqR+/z1SUdVLXChTkjIZIZMmTWL79u3Ex8dz7NgxJk2aRFRUFIMGDbpnsS8ZGUujUKhwcWkBQEzMUPT6LMsqVE6cbNwIbbKZ1Py2iAZvrJ0kkrMnEbNzlkX0kRDk5e9raFNNfgnndRVfSVjmVuxbtaLm6lW4DhlC2vezOf9EP/Kjoy2tlsxdKJMRkpKSwuDBgwkJCaFjx44cOHCA9evX0/naHdjYsWOZNGkS48aNIzIyks2bN98o9iUjUwWIqPcDdna1Sc/Yzq7dbcnMOmBplcpFgIMXA3vNo89zm6nmORqA1KuWSW8tR8fcYPK3awCoa1u1E+U9zChsbPB8YzyBy5ahtLfn4qDnSHpvMsbsbEurJnMb5AJ2Mo8kRUVpHDv+Orm5xwgMHINftedQKh/MfAMn9v9Cct409Hk2BPr/j5D6z1Tq+KFvLaO3v4Lpo5+o1HGrInXfWIajUce+mc9aWpVHHtFo5MSaNWRu2Ij+1EkURhHfl16k9nPPySt3FUR55m/ZCJF5ZDEYtMTGfUbR0XlEnMhGr1aiV6swRZsKxd6WkiBce3+n9ms/aIKAVNyPa+2mhyTcaHdMSQEg28v7P/34j4fnTWNeG0+41i4JN/UBdLokJCSMRUqOJ7TmYlHADZn/Rbjjm5tab7RLd2i//vIL6yY4KhX42TmV7H/TiTfLuN0Pjjl+hKSbpBRqDYgGERtHTYkJR7h5rFsvpfiNdLv2mz/bm44rBQGlYHrem5VHZHYGI60ldEVw2egHgMJ43TdBuv5p3qT4jbZC6yyyXeJQX8/0W3wMBOnGecJ/j/3n/X8pzZRb3mm5omSX5hxJEJC4/rcI4k3vCxUKjCrVLecEBwfz+OOP4+zsXA6tZO5GeebvWz8hGZlHBJXKjjoh71Ng1RJODECtN6INbgKSaJrQJBGQQLr2WpKuvb/96+JJovjYTa8Bofj9NZSqG+9vehak69OphHB9spK4/WskkMAGe0SDAZscLbXUpzlfEFA8bkmkEk+mWbksZsGt7ZHKZDI1Ncg33FrTQ7jD69u3CXdovxaBcweNbidCMpgmZMkolThRuvbv9V+KP7r/iCr+9O5wryYBOglEJEQJAowQKdhxNVdElARUQgE13bKxd7UuKf+avJvFShIcz9MiKiUaOrsXj3vj20BxPSRJumFySSVkSbeMcfP1lfz872z63e3e9NZDpTchbyf3dkbrf2VKdzgm3OOhVihoNWwYklKJQZS4mpHOuo0bmTVrFh07dqRp06ZyhlsLI6+EyMgA7PoaNk6G1w6B24Prw5T8cU8EjHi9s87SqsiUgz9/+ZszF48w+f3/WVqVh5bCwkK2bNnC/v37qVatGr1798bLSy4+aA4qNW27jMxDRfi1RFuHF1pWj/vFtC5vaS1kykFqYianEqKRkKvEViTW1tb06NGDYcOGodPpmDNnDps3b0av11tatUcS2QiRkQFwrAb1B8HOL2H3t5bW5j4QTNs+Mg8ciZeuAiAJIit/32BhbR5+/P39GTFiBG3btmXXrl388MMPxMfHW1qtRw7ZCJGRAZNDaJ9ZYO8NG/4HH1eDP5+HvT/Aqb8h/0GpSyF7/T+o1Aqrgb3SA4AL8XKys8pApVLRrl07RowYga2tLfPnz+fvv/+moKDA0qo9MshGiIzMdQQBXtoE7f8HdXpCQSasmwh/PgdfhcOFik/FPXv2bCIiIoqzB7do0YJ//71RmTUuLo4nnngCDw8PHB0dGTBgAFevXr35IvDU7+PKgo9uPH77hIKriRWuu8z9YWtvQ03/WgA0aNjQwto8Wnh6ejJ06FB69OjBsWPHmDVrFqdOVd2q2w8TshEiI3MzztXhsQnQby68sAZej4HnloFPJCx8Eo7+VaHD+/n5MX36dKKjozl48CAdOnSgT58+nDhxAq1WS5cuXRAEgS1btrBr1y6Kioro1asXomjyIzC41kGUFHic/674US1uOhkbf61QvSub7du306tXL3x9fREEgZUrV96x74gRIxAEgZkzZ1aafuXFKBoQJAXtujeztCqPHAqFgqZNmzJq1Ch8fX35888/+fPPP+XK7hWMHKIrI3M3XANND/+WMKspLH8ZRCPUH1ghw/Xq1avE+2nTpjF79mz27t3LlStXiI+P5/Dhw8We5wsWLMDFxYUtW7bQqVMn/F75DPgMzU0yDFM87hhi+qCi1WqJjIxk2LBh9OvX7479VqxYwd69e/H19a1E7cqPUTQiCSIGgxGVSk79bgmcnJwYOHAgJ0+eZO3atcyaNYvOnTvTsGFDOZy3ApD/RWVkSoPGFl7cAEoN/DsR4rZW+JBGo5HFixej1Wpp0aIFOp0OQRCwsrIq7mNtbY1CoWDnzp13lKMSilBd2oEkPjxRF927d+ejjz7iiSfunKX1ypUrvPbaa/z++++o1epK1K78KK9NcqnJD4oP0sOJIAiEhYUxevRoQkNDWbNmDQsWLCAtLc3Sqj10yEaIjExpcfSF8afBqZppa2bP91ABE/uxY8ewt7fHysqKESNGsGLFCkJDQ2nevDl2dnZMnDiR/Px8tFotb775JkajkaSkpDvKM0pKvIp2YXzfk9zYk2bXtyoiiiLPP/88EyZMICwszNLqlBovH1O+CqVK/mmuCtjY2NCnTx+GDBlCbm4us2fPZvv27RgMBkur9tAgf9NlZMqCnRsMj4Jmr8D6SfD7U5CXYtYhQkJCiImJYd++fYwcOZIhQ4Zw8uRJPDw8WLJkCX///Tf29vY4OTmRlZV1z2Vi4/g4koVGqAQ9DgtbcGnW+IdqVeR2fPrpp6hUKl5//XVLq1ImjNeyzqrU8k9zVSIwMJCRI0fSokULtm7dyty5c7l8+bKl1XookH1CZGTKisoKun0CQR1h5UhT5IxLANRoCX5NoCADQvuanFzLgUajITg4GIBGjRpx4MABvv76a+bMmUOXLl2Ii4sjLS0NlUqFs7Mz3t7e1KxZ887ynFzwnrKFtKhVuEcNpnrqzyR+nYmm6bNIClOCa0GpxiWyGcqbtnoeVKKjo/n66685dOjQA1eoLCPNtA2jVMpGSFVDrVbTqVMnwsLCWL16NT/99BPNmjWjQ4cOJbZIZcqGbITIyJSXWp3g5S1w+DfQppryiUTPMx2L+hSGrIJqje57GFEU0el0Jdrc3d0B2LJlCykpKfTu3fuectzb9cHYLInC6eH4Zi+HjctLHL9yeiLVBr9z3/pamh07dpCSkoK/v39xm9Fo5I033mDmzJlVOiGV4to2jL2DvYU1kbkTPj4+vPTSS+zbt48tW7Zw+vRpHn/8cWrVqmVp1R5IZCNERuZ+cK4O7a9N3D2+AFEPujzTNs2yl0y1aMpwNz5p0iS6d++Ov78/ubm5LFq0iKioKNavXw/AvHnzqFu3Lh4eHuzZs4cxY8Ywbtw4QkJCSiVfaWOLatxBspMSTA2iqUif8s8BKAofDqe7559/nk6dOpVo69q1K88//zxDhw61kFalQ6k0/SQ/aCs4jxpKpZKWLVtSt25d/v77b37//Xfq169P165dsbGxsbR6DxSyESIjYy4UClBYmbZrGjwH/4yHq8fBu16pRaSkpDB48GCSkpJwcnIiIiKC9evX07lzZwDOnDnDpEmTyMjIICAggHfffZdx48aVSU0rZ1esnF1LtKVL9igLU8m/fBEEARsfP4QqHI6Yl5dHbGxs8fsLFy4QExODq6sr/v7+uLm5leivVqvx9vYutbFmKRTXqwA/ZCHVDysuLi48//zzHD58mPXr1xMbG0vPnj2pW7eupVV7YJCNEBmZisDGxfRcRqfVn3/++a7Hp0+fzvTp08ur1R0pwhGfjNXw02oArvi8QrVXPjP7OObi4MGDtG/fvvj9+PHjARgyZAjz58+3kFb3z/UVENkIeXAQBIGGDRsSHBzMmjVr+PPPPwkLC6NHjx7Y2dlZWr0qj2yEyMhUBJnxppwi1ZtaWpNSYTP4Z67GHwPAduc7JufaKky7du3KNFFXZT+Qm5G3YR5cHB0dGThwIMePH2ft2rV899139OjRg/DwcPlzvQuyESIjY26yLsHOr0xVea0cLK1NqXCuVRtq1QYgc9dHFtbm0UW4th8jPuQh1A8rgiBQr149AgMD+ffff1m2bBnHjx+nZ8+exVmOZUoiGyEyMuZm23TQ5UDHyZbWpFxIgpJqWUtg6hIkSUBCQEKBUjCQZN0Bn7dXWFrFh5biO2Z5N+aBxt7env79+xMeHs4///zDrFmz6Nq1Kw0aNJBXRf6DbITIyJgb47Vsinu/h/bvlik6piog9PqGxHMHQJKQJFP0jCBJeJz9EttCucR8RXJ9gpJXQh4O6tatS0BAAOvXr2f16tUcP36cXr164eLiYmnVqgyyESIjY26e+AFEA2yfAee3QaMXIOJpUD4Yf24uDVvi0rDlLe1XZp7BNutg8XtJFBH1RShUagSlXGzNHCiuGyFGeSnkYcHGxoa+ffsSHh7O6tWr+f777+ncuTONGzeWC+IhGyEyMuZHEKDfj1CnB+z/EVa9ano0eA48QyF2k6kSb8TTEN4P1A9KXgEJF85TMNkfpaBDRRFKQSTJpjM+E5daWrmHA3kl5KElODiYV199lU2bNrF27VqOHz9O7969ixMPPqrIRoiMTEWgUED4kxDWD6Lnw9UTcGwJHF5oSvHuXMNkmPwzHlqNhfaTLKzwvbFtP5wre5xAbQ1qWwS1Nb5nPkBh1FpatYeGGzt3D9YWnkzpsLa25vHHHyc8PJxVq1bxww8/0L59e5o3b47yEV1NlI0QGZmKRBCg8bUsnd0+MYXuugaZjJSUUyajZNt0CO4E1ZuYdWjDpVgSX3uZ/NiruPdqivvH8+7LP8UlsikukSVDjrOmLsRg43O/qspcQ5KTlT0SBAQEMHLkSLZu3cqmTZs4ceIEffr0wcvLy9KqVTryhpSMTGWhVIN7LZMBAuBZFzp/CGpbuLzfbMPoL8cT27AW5zr3Qns6GckgkbpiH+dbhXL16VAKo/4y21j20mWqZS8jZ0oYBVOqk/zJ4xi0eWaT/6iRn22qEaR4iAvYzZ49m4iICBwdHXF0dKRFixb8+++/xcfbtWuHIAglHiNGjLCgxhWDRqOha9euDBs2jKKiIubMmUNUVBQGg8HSqlUqD+83XUbmQUChAGsnKMw2izgpP4fYTt3R55sWOX0njqDOieP4vTcc6xqeZJ82cmHEFE7VqcvpeqEkdKhNypuDMKYmQjnuvq8Gvs4Vp37kerTHRsjBW7eDtG2rzXItjyIqjeknWa15eJfm/fz8mD59OtHR0Rw8eJAOHTrQp08fTpw4Udzn5ZdfJikpqfjx2WdVN3vv/VK9enVGjBhB69at2bZtG3PnziUxMdHSalUa8naMjIwlMRSZUrvbl20ZVtLr0B/bg2BnS/a8b8hYfxDnpn5oTyUB4DGgLe4fzCnu7zBoHA6DxiEVFJD2v6Gk/XsEhUJEm6hEm3iI9DUd0TiDc5cWuH3wS6n1qPbCe8WvdWnvYvVdnfLYMjLXuB6i+zDnkujVq1eJ99OmTWP27Nns3buXsLAwAGxtbfH29raEehZBpVLRoUMH6taty6pVq/jxxx9p2bIl7dq1Q61WW1q9CkU2QmRkLEn8DpCM4HfDH0QyGNAd2IQ+7hRikQG1hyvq4HDUdZsV90kc1Jmco6k3CRLI2puAylrEvUdD3P4387bDCTY2eHyxGI8vro0liuT98iFiYREZK9aT8tcecraF49i2Kfa9n0PTuH2pJ0TJqDe9UDzcP5oVy6NlwRmNRpYsWYJWq6VFixbF7b///jsLFy7E29ubXr168d5772Fra2tBTSsHHx8fXn75ZXbv3k1UVBSnT59mwIABD7WviGyEyMhYkOxFP5J/0AlF8rvoU9PQXcnGWChiLLx14rf1t8Zt8NPYDXwTQW1attc4K/D7ZibqsOYo7MqeIl5QKHB4aQoAjqM+Im/hl2SvWErqst2kLNmD2klA7aRGoQJUNliHh+I69gOUnn63yjIUASDJRkj5eXgXQEpw7NgxWrRoQWFhIfb29qxYsYLQ0FAAnn32WWrUqIGvry9Hjx5l4sSJnDlzhuXLl1tY68pBqVTSpk0b6tSpw48//sju3bt54oknLK1WhSEbITIyFsKYl0fioiOAHYqL51DaKLAN9kLl6YrdY52ximiOYKXBcDWFwv1byPhzNZc+WoD13N9wG9CTosurKbgqonByKZcB8l8EQcDh+TdweP4NDFfOU7htObnr1mHI04EEkk5L+qrdpK3ohJWbBrfnn8Hx5bcRrjnaCqLJCBEF2Qi5X4SH3BoJCQkhJiaG7Oxsli5dypAhQ9i2bRuhoaEMHz68uF+9evXw8fGhY8eOxMXFERQUZEGtKxcPDw+sra2xtra2tCoVimyEyMhYiIx58wGo8dt8bCLqI1hZ3baf0i8Uq0btcBzxPtrVv5L+/bdc+e5vrt82F+6LQh3S2Ky6qarVxP7ZN7F/9s0S7frYGPKW/kjezt0kfvUbmX8txn3YYGza98Vw/gRSjpKM+EQyzh5HUKlwsLEnwOfWVROZu/Owh+hqNBqCg4MBaNSoEQcOHODrr79mzpw5t/Rt1sy0DRkbG/tIGSFg8hV52JGjY2RkLIQhIx2Ai4OHYsi+d3SMIAjY9xlCjfUH8f94LAAKjYRt1wF3PW/69OkIgsDYsWOL2+bOnUu7du1wdHREEASysrJKpbM6uD4ub8+i+t+HqD51JJIocOnDnznboRcXX5nM+bVefHZlJoP2DOTZHf3ptaE7f2xbwaGzxzkSd5Lj58+QkVO6sWQeHURRRKfT3fZYTEwMYPKXeJQQRRGtVotGo7G0KhXKw29mychUUbwmTMAmLIyk/71HbMdOBP2zBo2/f6nOtev3CiHtnwAEFC4ed+x34MAB5syZQ0RERIn2/Px8unXrRrdu3Zg0qRzZWgUB+2dex+7p1yjYuBR9QhxKjUi+5EJe0vdUK7BjeMRkpp6dxMfxkyG+5OltFF15re0I6tYILvvYMg80kyZNonv37vj7+5Obm8uiRYuIiopi/fr1xMXFsWjRInr06IGbmxtHjx5l3LhxtG3b9pbv8KOAJElcvnyZtLS0hza9u2yEyMhYCIWtLc5PPYVt06bEdelK3s6duD77bOnPd/G86/G8vDwGDRrEjz/+yEcffVTi2PVVkaioqLKqXQJBELDt0r/4vT2gmjmP8CI3+rXsQZ1qtbmalYJRFDGKRrbH7mR3znZ2aNbjvsuLoYbnUCmVKFVKVCoFapUalVqJSqlEpVaiUAkoFEKJCJ3CgjzSr57HycWD7IzLGI2iaftCAtP/pGthwlJxu4RoepYAxOLtDtNxUz+Ja32l6zLE4irC1ysKS5KIxLX24nPEa/Il0/M1HW7LXXZZMrOLuHT54f9JTklJYfDgwSQlJeHk5ERERATr16+nc+fOXLp0iU2bNjFz5ky0Wi3Vq1fnySef5H//+5+l1a50FAoF/fr1459//mHWrFn06dOH+vXrW1ots/Pwf+NlZKo4+muJiWzqmfdOb9SoUfTs2ZNOnTrdYoRUNNcn+dAawYTetNrRtdFjwLs0+qUJK/iVFTt/vascQRKQBAm10Zrvms7GWm3DiSPjcHSJw/4hy+e1L6YfRUV2KCXNQ11H5Oeff77jserVq7Nt27ZK1KZqU6dOHYKCgli1ahXr168nLCzsocsbIhshMjIWJvP3RWgCArAOCzWbzMWLF3Po0CEOHDhgNpmlRUAwrRDchZ+6/ER8WgJGo4jBaMRoNGI0iqZn8fqzqX3nld2cVB/kleihN8YosGGgaxEd7MbjVa3BtZUS4VppHNNrBG5pFwRFcbvi+uqKYGov7qO4njBMaWpXCIAChaA0vRYUKFCAIJhKsQvKm1KMm865a6ztHQ7t3fM+ERE29O71xkOdtl2mbKjVajp06MCJEyfYvHkzXbp0MX3vHhJkI0RGxoLoYmPJ3bgRhZNTcajr/XLp0iXGjBnDxo0bLRLeZ5pj726ENPCLpIFfZKnkjRJf5NC542iLtOiNeoqMepZcnMaqrFReatcVD4fA+9a5aiCgVAqo1PLPskxJXF1d6dq1K+vWrePSpUv06NGDatWqWVotsyB/22VkLIjG3x+lqyvGjAxyt2zBoUOH+5YZHR1NSkoKDRs2LG4zGo1s376d7777Dp1OV+HL/eYMMFUoFDQOKblVFRnsR99VT/Hpztf4vPsaM44mI1M1ad68OT4+Pqxdu5Yff/yR4OBgunfvjpubm6VVuy8enjUdGZkHEEGjodaunQi2tqT/XPqaLXejY8eOHDt2jJiYmOJH48aNGTRoEDExMRVugCgQ7pnnYurUqbdUSq1Tp07x8XtVUq3mFMLzwR3YkBLPgYsPSSZNgXtuY8k82tSoUYPhw4fTs2dP4uPj+fvvvy2t0n0jr4TIyFgY/aVLSIWFOPV63CzyHBwcCA8PL9FmZ2eHm5tbcXtycjLJycnExsYCpjTaDg4O+Pv74+rqel/jl8YnBCAsLIxNmzYVv/9vYqaXX36ZDz74oPj9f2uHjGj2Kf8ktGTavo9Z5tcLpfLBdtgT4Fo0jozMnVEqlTRp0gS1Ws3KlSuJj48nICDA0mqVG3klREbGwmTMn4/S2Rmnvn0rbcwffviBBg0a8PLLLwPQtm1bGjRowOrVq80ivzT38yqVCm9v7+LHf/MgXK+kev3h6OhY4rhGac3bTSYQV6Djt+h3zaK3ZRGQSxDLlJbr2WOXLFliYU3uD9kIkZGxIPmHD5O56A9cBj2LogKdSKOiopg5c2bx+6lTp5pyY/zn8cILL9z3WEIptmMAzp07h6+vLzVr1mTQoEEkJCSUOP7777/j7u5OeHg4kyZNIj8//xYZ7YMG0sK1GnPP/Et63oX71t3SmPKMyMjcGwcHB3r16oVWq2XPnj2WVqfcyEaIjIwFSftuFgAuTz9tYU3MR2miY5o1a8b8+fNZt24ds2fP5sKFC7Rp04bc3FzAVEl14cKFbN26lUmTJvHbb7/x3HPP3VbWe22+RifBZztHm/dCLIG8HSNTBq5HyKxfv568vDwLa1M+ZCNERsaCWNWuDcC51m04FV6P+GcHkf3334i3uet/UDD5hNyd7t27079/fyIiIujatStr164lKyuLv/76C4Dhw4fTtWtX6tWrx6BBg/j1119ZsWIFcXFxt8iq7hzCoKAOrLt6kUMXl1XAFVUORqOS2NgHcyKRsQze3t7069cPW1tbFi9ejNFotLRKZUY2QmRkLIjnWxPwnDgRpz59cGjfDkGjIXHCW5xt1ZqUr2ZaWr1yU9YoD2dnZ2rXrl3sKPtfbq6kejtGNfsUD42Gj/d/jNF4+0JoVR17ez1qtewTIlM2IiIiGDhwIFeuXGHfvn2WVqfMyNExMjIWRBAE3Ia+UKKt6NIlMhf9QfqcOWj8/XF+sp9llCsnginOo0zn5OXlERcXx/PPP3/b4/eqpGqlsmZi4wmM3/0xfxx6l+eafF6m8asCHh42pKTmWloNmQeQ6tWr4+XlRXJysqVVKTOyESIjU8XQVK+O51sTMGZmkvTuu1jVro2muh+62FiM2dkYs3MwZqSjO3cOY04uYmEBUkEhYmEhUuG154ICEATUNfyxiYxEodEgFhSisLFG7e+P0+OPo/hPyKu5EAQQ7+Ha8Oabb9KrVy9q1KhBYmIiU6ZMQalUMnDgwHJXUu1cayBNTs1nzpl19KgzEleHoDLpvX37dmbMmEF0dDRJSUmsWLGCvjdFLOXl5fH222+zcuVK0tPTCQwM5PXXXy+Rv+R+UCqViA/earpMFUCSpOLvZFFREWq1ukTRx6qMbITIyFRBBEHA58MPyF65kvj+/W85rrCzQxMchMrVDZWLC4KPDQprKwRrGxTW1gg21mAU0Z09i3bHTiSDAYW1NYbUVIzZ2Vyd9jHW9cKxCgzEtmkz7Nu3R2lvVyrdxPx8jFlZqHx8bvmhE41GLljlUiff+a4yLl++zMCBA0lPT8fDw4PWrVuzd+9ePDw8KCwsLHcl1Sltv6Hv6qf4fNdrfNxtbamu5zparZbIyEiGDRtGv363rj6NHz+eLVu2sHDhQgICAtiwYQOvvvoqvr6+9O7du0xj3Q5raxVFRQ92rhMZy2AwGFCr1ezZs4c9e/bg4+ND+/btqVWrVpU3RmQjREamiiKo1fj/uoD8vXtROjtj27QpKk9PlPb2CBpNueUWXb5C7vr1FJ48ScHRY2QtWQqAJjAQhb09+qQkxPx8rEPrYt+qFTYNG5G3ZTMZC35F4eiImJMDgMLREaWjI3atW+H20kto/PyIP7mHLBsRq/y7Z2VdvHjxHY/dTyXVGs4hDKzZgUVxmxlwcQn1a9xqwN2J7t2707179zse3717N0OGDKFdu3aAyXl2zpw57N+/3yxGiI2NBr1ejSiKD1WBMpmKR61WM3LkSOLj4ykqKuLQoUMsWrSIJk2a0KJFi/tOQFiRyEaIjEwVxq5pU+yaNjWrTI1fNdxeHFb8Xn/lCkkffEDRuViwt8fl6adR2NqQf/gw6T/9jKj9priv20svUXjsKFa1aiForDCkp5Pzzz9kLVlKvr8HLzyTBkD34F5m1bksvNZsOv8mtOKT/Z/we7VeqFTmyb/SsmVLVq9ezbBhw/D19SUqKoqzZ8/y1VdfmUW+vb09kpRDQYEWOzsHs8iUeXRwcHCgXr16ADRs2JCNGzeyb98+Dh48SOvWrWnVqpVFClreC9kIkZF5xFFXq4b/nDm3tLsBkl5PUUICgpU1Gr/bV+30HD+O7L//5tiOlUAaE5YaOdh1F80Knsfdxv2251QkNmob3mryBm/tns6Sw+8ysMkXZpH77bffMnz4cPz8/FCpVCgUCn788Ufatm1rFvkODi5AItnZybIRInNfCIJAly5daNeuHbt37yYqKoodO3YQGBhIly5d7ujgbQnkNT8ZGZk7IqjVWAUF3dEAAVDY2OAyYABtv13EkeeP0OScRL2VJ+i5vCczDszgWOqxStTYRLfgZ2ng7MsPZ9aTmXv7sN6y8u2337J3715Wr15NdHQ0X3zxBaNGjSpR/+Z+cHT0ACA756pZ5MnIaDQa2rVrx6uvvkqvXr3Iy8vjt99+Iysry9KqFSMbITIyMmZDoVBQ5/gxOv6zl541e/LXmb94du2zfHbgMzILMytND0EQmNr2a7KNAjN3v37f8goKCnjnnXf48ssv6dWrFxEREYwePZqnn36azz83Tziwo6MnALk56WaRJyNzHU9PTxo1asQLL7yAUqlk7ty5FBYWWlotQDZCZGRkzIygUuFk5cTkFpPZN2gfbzZ+k0WnFtH2z7Z0WdqFBScWVIoeNV3q0K9GM1YnJ5Cny7gvWXq9Hr1ef4vDqFKpRLxXPHIpycrSAmBlLScsk6kY1Go14eHh5Ofn31KryVLIPiEyMjIVhkJQMCRsCN0CuvHvhX/ZmbiTzw9+TofqHajuWL1Cx158ZDobLu/FgEBi9llqeza/a/+8vLwSGVkvXLhATEwMrq6u+Pv789hjjzFhwgRsbGyoUaMG27Zt49dff+XLL780i74nTuxAqSyiTkg7s8iTkbmZ3NxcfvnlFzIzTSuSer3ewhqZEKTSlLusRHJycnByciI7O/uW0t0yMjIPNrlFuXRd1pVuAd2Y3GJyhY7V968mpBUVUN+lOp91/hNbzd1/T6Kiomjfvv0t7UOGDGH+/PkkJyczadIkNmzYQEZGBjVq1GD48OGMGzfOLLkYZs58C1s7geEvf3rfsmRkbub48eOsWbMGpVJJr169CAoKQq02f06a8szf8kqIjIxMpeGgcWBU/VFM3z+dx2s+TkOvhmaRm5R9ltzCVEQkJElCQqLAkE9da5Hvev5bKhnt2rXjbvdk3t7ezJs3zyz6/pfMzPNkZdkQWd+/QuTLPHpotVquXr3KhQsXiiNj+vfvj20FZUouL7IRIiMjU6k8E/IM6y6sY3zUeN5u+jYda3RErSjfXdni43OYFv3dHY4qCHfyLL+ilcjBg2sAqB/Zw8KayDwMnDx5kpUrVxancG/cuDHdunVDpap6U37V00hGRua2GI1GlMq7ZyJ9EFAqlHzZ7kt6rezFhO0TAHi/5fv0CeqDUlG664u5sp5vDk7nQJYpOdprIZ2o6dYABQKCICBce27g27nCrsNciKJITEwiPj4SLi5VJ3+DzINDQUEB2dnZHDp0iDNnzpCdnU3dunVp3749zs7OaO4jw3JFUyYjZPbs2cyePZv4+HgAwsLCmDx58i2pjiVJokePHqxbt+6WIlAyMjLlY82aNeTl5fHss8+axQdBkiTO7N5ORuIVbBwcsHVyxq1adVz9qqMopTFQXjxsPdj29DY2xG9g7tG5TNk9hfkn5tPcpzlKQUmoWygNvRpSzb5kfpKYy//yXfSn7MtKx12tYFRIF15oNBVr9YOb3Cs6ehVarTXdujWytCoyZaQskVFlcb8s7lvyCW6Scf3Vzp072b7dVOZArVbToEEDatWqRXBwcJWvGwNlNEL8/PyYPn06tWrVQpIkFixYQJ8+fTh8+DBhYWHF/WbOnPlAXLyMzINAYWEiGZm7cXW9iqPTWrZsnYyHRxdqBo7F3j6kVDJEnRFJZ0ThcKO65o5F8zmwehm2Ts4U5uUhGg0AWNs74OLtiygaUSiU1GrWkpoNm+JazQ9BEBBFI3EH9nHlzEly0lJwdPckqFFTqtUNK5PxYqW0oldQL3oF9eJY6jEWnFzAriu7AFh4aiEAIS4htPVry2PVH2P9qe9YeGEPriol40J7MajB/7BSVa397bIiSRI7d+7D1VVPePjjllZH5g789PUiktISkJBAkJAQkRQGS6tVTPNmLahTNwRfX98qvepxO+47OsbV1ZUZM2bw4osvAhATE8Pjjz/OwYMH8fHxKfNKiBwdIyNzg8SkpZw6NfGOx8PCZuLl+TgYJYw5RShsVYg6I9q9Seiv5iOoFUh6kcLTGSBKqH3s8BgRicJKyXfDnkY0irQaMIj6XXuiL9SREh/HpRNHyc1IR6lSoc3KIv5INEa9Hs/AIOq2eozjUZtIv5yAs7cPDq7uXL0QS1FBAQDjF/9tlhuQbF02+5L2sTlhM7sTd5OlywKgoa2BiZHP4WhbA2vrari4NEeptAFAFA3kF1xANBaiUGjI057FYMhFqbDGYMxDkoyIxgKsrH1xc22LRmPZol6iKHL06EZWrtxDnz51adDg6Vv67N54mLxc7bWJD0ACAZAkJEFCo1HTumMTNFb3P/EkJaRwYNfRe/QSuPHxCtx4KdzU4z+vhFt634KEdO3WXipx1198zVxfBJD+0/+mM6SbzkUq0f9O+pd49Z/v7c36Hj67CxuFM/7VAlAoFAiCAoWgwCDqsbW2LyHvv1dZQuwtY9yG6/9epfg7Sr6QTfZlA69O742VreUrMFdqdIzRaGTJkiVotVpatGgBQH5+Ps8++yyzZs3C29u7vKJlZGSukZNza8pzD4+uZGbuxWDI5sSJseSfSEazoU6JPoJagSbQCanQgKgXcepaA1FnJHfLJXTns1D72NFt5Dj2r15K1K8/ocvX0rL/IPzDI/EPjywhS68rJOH4UfYt/5PdS/9AX1hAu8Ev06hnHwASz57mj/feBEA0Gohed5kDay6gUAiIomkS6PV6JP6hbqW+bicrJ7oEdKFLQBeMopF9yfv45+R3NLbJIzNtPcm6q0iSAY3GnYAar5Kbd5KUlH8xGrW3lScIGkBEqbTDYMhGqbQjIGAUNfxfRhAqP2fjmjUzOXgwCwB7ey316z91S5/Eiyls2LXqnrK2798IgAI1yus/6QIoBRUNI5qWnKylktOyUW8kJSEXVx87jp7bj1HQ3Tpv30xVWOAuod8tU/6NV9Ltjl8zYm65jrtdtMnwqxMURp9BVcfHKOViDttiz1BQlItBL2JlaYXKSZmNkGPHjtGiRQsKCwuxt7dnxYoVhIaGAjBu3DhatmxJnz59Si1Pp9Oh0+mK3+dcKxMuIyMDtYIn4ezcGKMhD5XKkTztWdLTo3B2boJKaYegUKNc7geAVU0nbCI9UNqr0VR3ROlY8u5YF59N7pZLpC84CYBrfQ/6v/sR3w17hmOb11OjXgN8Q+recgemtrImsEEjrOzsOLN7B/lZmSTHneX3d8eTk5pCfnYWKisrer7+FkqVmqJ80zL1dQMEwMa+/HfqSoWSlr4taenbsrhNkozk58dzIf47zp77AEFQ4+//Em6ubVCq7JDEIpRKe+zsghFFPQqFaXxBENAVpXHx4hzi4maQn3+eunU+RhAqz+F3x47fig2QBg00hIV1KzG+JErE7DnDwX2HAejcphcNmoeaPpfi2VMACdYs2URqVhIp2Zeo7hYMkoAkSVxJP49ekcvumM2l0ulirEmkk8aLce+MvGd/SZJu8XG4+X1Zjl3/vl1/njNnDnPmzLmt72FGRgZTpkxhw4YNJCQk4OHhQd++ffnwww9xcnIq1bU+DOgKDCz55CAATXsFYuf0oJog5diOKSoqIiEhgezsbJYuXcpPP/3Etm3biI2N5Y033uDw4cPY25uWpwRBuOd2zNSpU3n//fdvaZe3Y2RkSochrYDc7ZfR7k8GwOe95ijtbr80q0/Jx5ilo+BkOtq9SXhPakpqSjzrvv+KjMTLAKisrKhRrz4qjRWGIh2iwUDSuTMUavOwc3bB2dsXAGcvbxw9vHDx9qF6WAT2rjdWOjKTtYhGCTtnK6zvoIu5yMzci42NP9bWvmU6Lzl5FSdOjic4aCI1agw3mz6iLof8M38iGgv54pf1/L0lhnPxaVhbqWkaGUhk4zA0diGMHz0MR3d/4uLiePPNN9m5cyc6nY4GEU1o0rwBjnbOeDr7MejF/jg42pVZj3xtydogxeaLcGObRJJANEqoVAoEhYBKrbwlNX1l8/fff6NUKkv4Hs6YMYPDhw8jSRJTpkzhhRdeIDQ0lIsXLzJixAgiIiJYunSpRfWuDESjyLFtV4hedxFjkZFW/WsR2qps3/uKpDzbMfftE9KpUyeCgoKwsbHhm2++KfEFNhqNKBQK2rRpQ1RU1G3Pv91KSPXq1WUjREamjKT+chzd2UyUjho8R9dH6Xjj7kiSJPRJWnI2JVB4Kr149dmpRyAObf0QRSMJR2NYOeNDjAYDgfUbYdDrUanViKKIe3V/ajVrjU9wbRQPQZjwdU6f/h9Jyctp1OgvHB3CzSIzc90wXPYuA6DbQi3PhKtp4qvEIMI7W3TEpAiMeHUU1ho1zWu588J7PxBZvz6vjxrHrq37WbRkAVl5GVy4cP6BczKsKP7re3gzS5Ys4bnnnkOr1VbJPBjm5Pi2y2z74yxBDTxo1b8WDq7WllapBBbJmCqKIjqdjvfff5+XXnqpxLF69erx1Vdf0atXrzueb2VlhZXVg7uUJCNTVfAYFk7RpVxSfzpG0qcHsG3gicrNhsKT6ejTCpAKDKAUSmx/20SayscrFEoC6jdizG/LMRoMqB6Rya9WrffIyT3O8eOv0bTJGlSqsq843Iw++3yxAVIwYj0rRyqv+SaYmDs6A7963WhQ1w9d+lXmrjvKhfh4Fn7+NocPx6JX5fJE3yf5eMb7bN++nU6dOt2XPg86t/M9/C/XJ7yH3QABUKhMN/nZaQVVzgApL2X61CZNmkT37t3x9/cnNzeXRYsWERUVxfr16/H29r6tM6q/vz+BgYFmU1hGRubOaKo74PN2U/L2JaHdm0T+watYh7nhEOqK2ssO6xAXBKUCSZJu630vKBSPjAECoFRaER42k/0HehF3fgYhtaeWS45Rl0XBDxHYZ2YDkOXlibP3rQXzCvJMBfICGnQlPDwc7XefsGgR/HMooXgSnTR5AtO/+JCdO3c+skbI3XwPbyYtLY0PP/yQ4cPNt51WlalZ34Otv50m7VIeBr0RlfrBX5UskxGSkpLC4MGDSUpKwsnJiYiICNavX0/nzlXHY1hG5lFHYaPCsV11HNr6mXKD2Nz6Zy7n8bmBrW0AATVGcf7CTPyrv4iNTdmq+0qiEeUnNbAHjALkPPYiLu1urawriiJjx46lVatWhIebtn66P/0S9u9O53z0bgIj2+CQXZs3Rr6M0WgkKSnJHJf3QBISEkJMTEyx7+GQIUPYtm1bCUMkJyeHnj17EhoaytSpUy2nbCWSeC4LAJVagV73CBohP//8c5mEV7ECvTIyjxSCQkC4jQEicyvVqj1L3PkZpKdvw8/vuTKdm7HpJdyAtLoNcX96Ky536Ddq1CiOHz/Ozp07i9s8PDxYsmQJI0eOZPH6KAQEmtVqRURILYs7iFoSjUZDcHAwAI0aNeLAgQN8/fXXzJkzBzCVpe/WrRsODg6sWLGiQirCVkWuR8G0HlDrviLOqhKP7rdcRkZGBjAa89l/oDcANrYBZTq38K+ncNu9nKwawbj1v3M47OjRo1mzZg1bt27Fz8+vxLEuXboQFxdHSkoKF0+dZczjo4hPSMUpL6usl/LQct33EEwrIF26dEGj0bB69WqsrR8O34jSYOukAQFyMwrv3fkBQTZCZGRkHmmuXv2HwsJLBASMxs219V37SpJIevp2jMZCJGMR1idNicKcBu9FuM3KhSRJjB49mhUrVrBly5a7+se5u7vjFxKESydncguy0ei78ee439kxYwGiXn9/F/kAMWnSJLZv3058fDzHjh1j0qRJREVFMWjQoGIDRKvV8vPPP5OTk0NycjLJyckYjUZLq17hOLhaE9LUm1O7kjAaSl+3piojr9XKyMg80nh4dOb0mfdISPgZrfYs9nYh+FZ7BmurG472RqOOnNyjJCb+SXLyCgAC47XUBArcfbFR3P6ndNSoUSxatIhVq1bh4OBAcrIpl4uTkxM2NqZ08/PmzaNu3bp4eHiwZ88exowZw9hxYxnY0p34mGSOxgUSdngHrk07VOw/RBXhbr6HUVFR7Nu3D6B4u+Y6Fy5cICAgwAIaVy4+wU6c2ZdMoVb/QCcpu8595wkxN3LtGBkZmcomKzuaCxe+xWDIJS/vJKKox9m5KQ72dcnJPUZOzjEkqajEOXZaA43jnVClX4TgzvDkj2BT0iPkTg7A8+bN44UXXgDg7bffZv78+WRkZBAQEMCIESMYN24cgiCQl57Hgnf3Y6fKwsFWh7uHSJPn2mHrU+22cmUefi6eSGfNt0do2iuQJj2rVuSpRZKVmRvZCJGRkbEkBkMuKanruXp1DVptLE6O9XF2boyzc1Ps7IK5VkEOQVCZ6s6cWQcrXjEZID0/h2DzhtWeWbqajPgktNkGLqb7IUoKGjfIIeL5fiivrabIPBqkJuSy5JMDSBJ0eTGMWk28LK1SCWQjREZGRsYSZFyAla9Cwm4I7QttxoNP5D1PKysFKSlEfbuG86kBNAm7TNPXBpt9DJmqy7Y/znB82xV8aznzxBsNLa3OLZRn/pYdU2VkZGTuF9dAGLoW+nwPiYdgTlv4tS+kxZp1GMloIDPHFhtFDnW6NzOrbJmqj0ptmrJDW/lYWBPzITumysjIyJgDQYAGgyDiaTi5ErZOg+8ag2ddqN7U1O7fwtSvHBSmXGXVp1HoDNb0G1MHx+AQ8+ovU+XJzzH5JTl52lpYE/MhGyEyMjIy5kSpgnpPQa0ucGo1XNoPcVshej5414OuH0Ng2zKJLMpM5+9PNpBf5MgTo4JwDqlTMbrLVGly0gqxd7XCo4aDpVUxG7IRIiMj89CSpTewOiWLU9pCNAoBW4UCoyThoFLSxd2J2rZWFZfC3toRGjxneogixG+H3wfAgl5Qsz30/hacS5cift0nK0kt8Kf/K+64hpmn2q/Mg8WVM5kknzfVJspNK8TZ6+FYDZEdU2VkZKokhiIjEqDWlK8+xom8AgYfPU9ykZ4gG2uMkkShKKIUBNL1BrRGEXulgr6eLrwR6IWPVSWkwdYXwslVsOVD0OVAn1lQ985VxgEkUWTxN29h5ZSIvYt1id0ciXw8PXtRv8WgClZcxlJIksSRzZfYt/o8Dq7WdH4xDI/qVXMlRI6OkZGReShY/+NxYqNTsHO24oXprcol49kjccTl61jWIBg/65IGRpEosjE9h5N5Bcy/kk663gBAb09nXvHzoKGjbcUW+SvIhNWvwam/oc0b0HHyHbsaC9KJ2tMUfZ47CuFaSKYkIQEq+5OmPjktcbRvTY3gLnj7ByAo5AKFDwt7V8YRve4itZt50bxPEA6uVTdNvWyEyMjIPBTMGrEFAHtXK4Z8XD4jZOSJeKJz8tnQuDbO6jvvPOcYjEw+d4XFyRnFbc2d7JhW248w+wrMwyEaYfXrELMQbFzBIwSajYC6vRBFPcfXt6JAyKNApceoEqjvOgK3+hNKiEi6eJy408vJL9qNwjYWQZAoyvHHijY0bT8WhVIwrZwIEgqFEoVCbXooVXIl5UpEkiQkJERJRJIkREREUSQvs/Bau6mN68ckEUmCxJPZHFqaRIt+QTTsUsPSl3FPZCNERkbmoSD9Sh5Ht1yiVf9aaKzL57p2TltIr0PncFQp+SOyJkG2976DNEoSW9Jz+Oh8ElcKi9jYOIRA2wpMjS1JcGYtxO+CpCNwcSd4R5DRuAeHc37Co8ARB/swvGq9gq1vm7uKKshP5cLpjSReWY1gd+DeQxuVSJIKJCWSpARJCVJJw+TWyeE/hsttZ4//GjdCcV/plvOF28i88V6UIOdqCJeP9gVAKdy0NXfL2MJtXt2u792Mr5tk3HVmLClDKId8pVS6bUa9cy5jPun9QBiNshEiIyMjcxMJBToGHjlPUpGeZ7xd6eTmSFMnO+xVd58AtAYjTfeews9azXd1a1DLrpKWwBP2wZIXOOOZzeVqNnRodxZBUTafGFGUOB3zD3pdDiAgSSZbB0lElAxIkh5JMlCQm4+gMCIJBsAAGBFumiSlW2Zh03vhP+9Lhhzf/hxTN+mmNqH4mIRUoq9JnOl1pn4XeimbFUc64m3nRTX7ajd0vGnYm9tKTNXXdBMomUL/jjKEW1/f3N8k4obMm69dcX0sQTA9uPUZ4Xo/ofg/jbNJjkIQmHtk7n/+/QQ+7PcOjfzr8yAgGyEyMjIy/yHHYGRm/FWWX80kuUhPcyc7Vjasdc/zorO1jDmdwOXCIuaGBdDF3akStAUyL3J0WzdSHYuwtw8lqOZ43NzaPRB3wubmj10D0OQf5cnOpy2tSqWw4MQCPj/4OV+1+4qO/h0fuM9czpgqIyMj8x8cVUomB/tyqGUo3dwduaLTI5bi3quRkx3rG9emjYsDY08nUGCspNLpLjUI73WU+pHzUakcOHL0JWJihlBQcKlyxq9SKFHcfV/koWJw6GA8bTzZm7T3gTNAyotshMjIyDwSKASBEdU9uVRYxKcXkinNIrCdUsmkmj5k6I3sy86rBC1NKJRq3Nza0LDB70TUm0N+QQL7D/QmI3NPpelQFRAUKhS3dzx56CgyFjFxx0RSClJoXa21pdWpNGQjREZGplyc2rWNv7/8hHnjRvDL2OEcXr+G3PQ0S6t1V5o72/NuTR++vniVxw+dY+6lFLak53AkN5+z2kKyr4XqXkeSJH5NTAfA20pd6foKgoCHRyeaNlmNo0MER468SHr69godc/bs2URERODo6IijoyMtWrTg33//LdFnz549dOjQATs7OxwdHWnbti0FBQXmV0ZQVrgRsn37dnr16oWvry+CILBy5criY3q9nokTJ1KvXj3s7Ozw9fVl8ODBJCYmml2PbF026y6sAyD6ajRpBVX7b8lcyBlTZWRkysX56P2c3bcLjxqBOHv7sHXeXLb88gMAz30yE6+awRbW8Pa8VsOLCAdbZiVcZdr5JHTijUlOAUQ42BLhYINRktibpSWuQEd7Vwfq2FVguO49UKsdiYiYy7Hjozhy9BUi6s3C3b1DhYzl5+fH9OnTqVWrFpIksWDBAvr06cPhw4cJCwtjz549dOvWjUmTJvHtt9+iUqk4cuQICoX572kVqFBWsBGi1WqJjIxk2LBh9OvXr8Sx/Px8Dh06xHvvvUdkZCSZmZmMGTOG3r17c/DgQbPq4WHrwQ+dfuC3U78x/8R80gvS+bjNx2YdoyoiO6bKyMiUi7SEeJZNn0pBTjZegcE4enhyete24uOPPf8ijR9/woIa3hujJHGlsIgsg5FCo0hsvo7tmbmcyy9EgUCYvQ1P+7jSwtne0qoCIIpFHD8xhrS0LdQLn4WHR6dKGdfV1ZUZM2bw4osv0rx5czp37syHH35Y4eMu2T8Sh9wNdOsYV+FjgWnlacWKFfTt2/eOfQ4cOEDTpk25ePEi/v7+Ztfh0NVDDFk3hBY+LZjb5b/RMlWb8szf8kqIjIxMuXD3D2Dwp99wcvsWkmLPknTu9LUkWGA0GHDzM/8PtLlRCgL+NlZc17Spsz3P+rpZVKe7oVBoCA/7hqNHh3P02CuEhn6Bj3ffChvPaDSyZMkStFotLVq0ICUlhX379jFo0CBatmxJXFwcderUYdq0abRubX4/BkFQ3jXrhiXIzs5GEAScnZ0rRH5Dr4b42fvhYetRIfKrGrIRIiMjU25sHBxp1LOvpdV4pFAo1ISFfcWZs1M5efINCvLjCQwcY9ZoimPHjtGiRQsKCwuxt7dnxYoVhIaGsnfvXgCmTp3K559/Tv369fn111/p2LEjx48fp1ate4c+lwWFoKpSjouFhYVMnDiRgQMHVuhKvSiJ2KntKkx+VUI2QmRkZGQeMNRqZ8JCv8LeLoS485+j0Xjg52e+InYhISHExMSQnZ3N0qVLGTJkCNu2bUMUTWHKr7zyCkOHDgWgQYMGbN68mV9++YVPPvnEbDoAKAQlCsHkIGzpkFW9Xs+AAQOQJInZs2dX2Dgn0k6QqE0kzC2swsaoSshGiIyMjMwDiCAIBASMRKe7yrnYj3ByboSDfR2zyNZoNAQHmxyLGzVqxIEDB/j66695++23AQgNDS3Rv27duiQkJJhl7JsRitO0i0D5qimbg+sGyMWLF9myZUuFrYJIksT/dv2Puq516RHYo0LGqGrIRoiMjMxDic5gZPH+S2w5nUJOoR57KxWeDtY8FuLB4/V8UDwklWZr1ZpERuZuYs99TIMGv1bIGKIootPpCAgIwNfXlzNnzpQ4fvbsWbp3727WMfWGQi5lHKSuwjS+UmkZI+S6AXLu3Dm2bt2Km1vF+QwlaZOIzYol2DkYleLRmJ4fjauUkZF5pJAkieG/RrMrNo1Wwe7U8rQnt9DA6eQclh26zB/7Evju2Qa42VdgcbpKQqGwIjjoTY4eG0lm5l5cXJrfl7xJkybRvXt3/P39yc3NZdGiRURFRbF+/XoEQWDChAlMmTKFyMhI6tevz4IFCzh9+jRLly410xWZOBS/gLqKi2QZFAhCxXmG5OXlERsbW/z+woULxMTE4Orqio+PD0899RSHDh1izZo1GI1GkpOTAVPEkEajMasu3nbetPNrR9TlKH448gMBTgF0qdEFpUJZJbakKgI5RFdGRuah49zVXDp/tZ3vnm3A4xG+JY7tjkvj9T8OU83ZhkUvN8fO6sG/F5Mkif0HHkehsKJRw79Q3Mdd9IsvvsjmzZtJSkrCycmJiIgIJk6cSOfOnYv7TJ8+nVmzZpGRkUFkZCSfffaZ2aNjdp3+isLE7whusJYaLiFmlX0zUVFRtG/f/pb2IUOGMHXqVAIDA2973tatW2nXrl2F6PTalteIuhQFQB3XOnjberMveR9qhRoPGw+erfssA0IGVMjY94NcwE5GRkYGuJyZT+tPt+Jko+bDvuH0jixpiBy/ks0zc/fSsIYLPw9pjFpZlWIwykd29mEORj9FWOhXeHv3trQ6983O01+iS5xFE0bkogAAD49JREFUrYbr8Hc2b9RNVSerMIt18euwU9uxOWEzhYZCIjwi0Cg1nEo/xYaLG1jUYxH1POpZWtUSyEaIjIyMzDV+3H6evw5ewihKbH7jsVuWsneeS2Po/P08VtuTr5+p/1CsiOzb3wt7u1qEhX1paVXum+2nv0Cf+D0hjTbg5xRkaXWqDEbRSO+VvUnITeC95u9VqRURuYru/9u7+6Cm7nwN4E+SkwTIG2ADtogIpSLq3IrWKnq1giJ22da71kp12sIy2Pfti95xtHWGq7vsbrvMqDPqzHXq0EudaqXbudKWGXQHbK2yvWLV1heqViyuNlZ8gQTQhJxz/+BloLybk5wkPJ+ZjPI7vzn5ni+EPJyXHCKiDivnJuCP/zEZFxuasfPrul7L//2h+7DjuUdw+EID0ooO4u/H/jWkm9r5s1Gj5uLGzUMBvx0AgI5tUHvxfJBApFFrsOqRVQCAqstVClfjOX53iShozUwYhd/PHoc/fXEWp6409lqeNiEKB1bNxfT4SKwuPYnndv4f6m+0KFCpPCIiUuFy3URz8zmlS/GY2BFCVH73manKmxDZfil2qKDc/YzkwhBCREFt3ePJGGXQYfM/zqHNLfZaPiYiDNtWTMUHv5+OuoZmZG7+Cu8fugi3GHh7E8Itj0Ct1uHmrSNKlyKDjhAShFeEeCrGGIOFcQvx7bVvlS7FYwwhRBTUdIIaf33q33Dwh+t49aNv+wwiADAvKQr735qL7OmxKCw/iyXbD6PW1uTjaj1z61Y1RNEJ0X1X6VI8JjKEDMiitwTF/WUYQogo6GVMjMZ/PzcNFaev4bG/Hez3nAmDXsB/PTkJf395Fpqdbjyz45+w33H5uNp743Ldwsnv8qFSaRAd/Vuly/GYxHNC+uRyu7Cndg/K68qRGJ6odDke43eXiEaE+cnRAIArt1vxzI5/4nj9rX7nTh0bgdfnP4TbLS5cvX3HVyV6RKUSIAhmCIIFgmBUuhzPdYYQvk11udN2B7kVuSj8phCLxi3CuhnrlC7JY4F/TRoR0RBdKHwcX19owF/Ka/G77UfwzPRYrF6YBKtJj+v2u6g4bcMdlxu1NjvKTl7Fo/GRSIwKjDd0QTAhZcr/4GjN72Cz/S9iY3OVLskjIiSowcMx3e27sA/fXf8OJY+XICUqRelyZMEQQkQjhqBRY15SFOY8ZMXHRy9j4+ensbfmMqwmPRocToiSBL2gRmxEGP6QloiVcxOgCaB7zISGjgUAnDv/x4APIRJG3uEYl9uFTd9uQrg+HOPM4zDaMBpqlRqnGk6h5EwJLtsvAwDizHEKVyofhhAiGnE0ahVWzBiLRZNH48AZG35uvINocwgWTRqNCIO89wPxJY0mDADwYMJ/KlyJ5+60XIQBgFYdmN8PURLR7GpGs6sZdqcdza5mOFwOOJwOOFyOPscv3L6AS02XoFFp4JbcPdYnqATMGD0DWQlZiAyJVGir5McQQkQjVqRBh+zpY5UuQzYqlRZabQTsjjOou7QNouhEdNRvYDR6794r3uKUNDAAMOgjfPq8oiSita21Kyw4XA40O5thd3UEhu7jAwSMZldzv8+hggoGrQEGrQEmnQkGrQFGrRFJkUlYN2MdUu9PRePdRthabPil5ReMjxiPqLCooNwrxBBCRBQkVCoVrPdl4OrPe/HLL+XQaiNw6dJ2JI0vwJgxzypd3rBIENEiDv9QmMvtgt1lh93Z/mhyNsHhdPT42u60w+HqOdYZNhwuR9ehoL6ECWEwao0w6owwao3tAUJnRHRYdK9Q0X1O9/EwbdiggSI8JBzhIeFdH0wWrBhCiIiCSFzcS9BowjB2bD50ulE4f/7POHd+IyyWFJhMk5Qub1Ci6ISrrQlqsQWiJOHrK1/3DhAdoaLJ1fNru9OOO+7+r2Yyao0w6Uw9HjHGGEzQTYBRZ4RJa4JB1xEgOkKEQWvoGjcIBmjUGh92I/jxBnZEREFMFNtwpHoeRkXOQXLyX3z0nO1Bos3ViLa2RrhcjWhra4Kr6/+NaHM1ds1xtXUsdzVCFFu71tPQpsKffm7/aHK1St0eHLSmXkGi6zHAMgYI77uX92/uCSEiCmJqtYCQkBhc/XnvsELIQEGiV4AYIEh0p1LpoNW2f5aJVjBD0FoQEvIAjNoJ0ArhELQd44IFKo0Rt9xaHJgdA7POjFAhlJfrBiGGECKiIBcVtQiNjTW4fv0fcLub+w4Sv/q/2933jfxUKi0EwQytNrxXkGgPFxYIWnP7v4IFWq2lI1xYoFaHDCtI3CdXA8hvMYQQEQW5yIhZAFT47vsXAXQPEp1BwQy9fjSMxqT+g0THfLWaeyRIPgwhRERBzmhMwtw5NRBFFwTBwCBBfoMhhIhoBNBqw5UugaiX4PvkEyIiIgoIDCFERESkCIYQIiIiUgRDCBERESmCIYSIiIgUwRBCREREimAIISIiIkUwhBAREZEiGEKIiIhIEQwhREREpAiGECIiIlIEQwgREREpgiGEiIiIFOF3d9GVJAkA0NTUpHAlRERENFSd79ud7+ND4XchxG63AwBiY2MVroSIiIiGy263w2KxDGmuShpOZPEBURRx9epVmEwmqFQqpcvpU1NTE2JjY3H58mWYzWalywk67K93sb/ew956F/vrXZ72V5Ik2O12PPDAA1Crh3a2h9/tCVGr1RgzZozSZQyJ2WzmC8GL2F/vYn+9h731LvbXuzzp71D3gHTiialERESkCIYQIiIiUgRDyD3Q6/UoKCiAXq9XupSgxP56F/vrPeytd7G/3qVEf/3uxFQiIiIaGbgnhIiIiBTBEEJERESKYAghIiIiRTCEEBERkSIYQgZRWFiIWbNmISwsDOHh4b2Wnzx5EsuXL0dsbCxCQ0ORnJyMLVu29Lu+w4cPQxAETJkyxXtFBwg5evvpp58iIyMDVqsVZrMZqampqKio8NEW+De5fnYPHjyIqVOnQq/XIzExER988IH3iw8Ag/UXAF5//XVMmzYNer2+39d8RUUFZs6cCZPJBKvViqeeegqXLl3yWt2BQK7eSpKEoqIijB8/Hnq9HjExMSgsLPRe4QFCrv52unDhAkwmU7/rGghDyCCcTieefvppvPzyy30uP3bsGKKiorBr1y6cPn0a77zzDtatW4etW7f2mnv79m08//zzmD9/vrfLDghy9Parr75CRkYGysvLcezYMaSlpeGJJ57A8ePHfbUZfkuO/tbV1SErKwtpaWk4ceIE3nzzTeTn5zPoYfD+dsrLy0N2dnafy+rq6rB48WKkp6fjxIkTqKioQENDA5YsWeKNkgOGHL0FgDfeeAPvv/8+ioqKUFtbi7KyMjz66KNylxtw5OovALhcLixfvhxz5sy5t2IkGpLi4mLJYrEMae4rr7wipaWl9RrPzs6W1q9fLxUUFEgPP/ywvAUGMDl6293EiROlDRs2yFBZcPCkv2vWrJEmTZrUY052draUmZkpZ4kBbSj97e81X1paKgmCILnd7q6xsrIySaVSSU6nU+ZKA48nvT1z5owkCIJUW1vrneKCgCf97bRmzRrp2WefHdbvme64J8QLGhsbERkZ2WOsuLgYFy9eREFBgUJVBYe+etudKIqw2+0DzqH+/bq/1dXVWLBgQY85mZmZqK6u9nVpQWnatGlQq9UoLi6G2+1GY2MjPvzwQyxYsABarVbp8gLaZ599hoSEBHz++eeIj4/HuHHjkJ+fj5s3bypdWtCorKxEaWkptm3bds/r8Lsb2AW6I0eO4OOPP8YXX3zRNXb+/HmsXbsWhw4dgiCw5feqr97+WlFRERwOB5YtW+bDyoJDX/212WyIjo7uMS86OhpNTU1obW1FaGior8sMKvHx8di/fz+WLVuGF198EW63G6mpqSgvL1e6tIB38eJF/PTTTygtLUVJSQncbjfeeustLF26FJWVlUqXF/Bu3LiB3Nxc7Nq1y6ObCY7IPSFr166FSqUa8FFbWzvs9Z46dQqLFy9GQUEBFi5cCABwu91YsWIFNmzYgPHjx8u9KX7Hl739tY8++ggbNmzA3r17ERUV5emm+CUl+zsSeKu//bHZbFi5ciVycnJw9OhRfPnll9DpdFi6dCmkIPswa1/3VhRF3L17FyUlJZgzZw7mzZuHnTt3oqqqCj/88INsz+MvfN3flStXYsWKFZg7d65H6xmRf5avXr0aubm5A85JSEgY1jrPnDmD+fPn44UXXsD69eu7xu12O2pqanD8+HG89tprANpfHJIkQRAE7N+/H+np6cPeBn/ly952t2fPHuTn56O0tLTX4YNg4uv+jh49GteuXesxdu3aNZjN5qDcC+KN/g5k27ZtsFgseO+997rGdu3ahdjYWHzzzTeYOXOmbM+lNF/39v7774cgCD3++EtOTgYA1NfXIykpSbbn8ge+7m9lZSXKyspQVFQEoP1KJFEUIQgCduzYgby8vCGtZ0SGEKvVCqvVKtv6Tp8+jfT0dOTk5PS6/MtsNuP777/vMbZ9+3ZUVlbik08+QXx8vGx1+ANf9rbT7t27kZeXhz179iArK0u25/ZHvu5vX4cGDhw4gNTUVNlq8Cdy93cwLS0tUKt77pDWaDQA2v9YCSa+7u3s2bPR1taGH3/8EQ8++CAA4Ny5cwCAuLg4n9XhK77ub3V1Ndxud9fX+/btw7vvvosjR44gJiZmyOsZkSFkOOrr63Hz5k3U19fD7XbjxIkTAIDExEQYjUacOnUK6enpyMzMxKpVq2Cz2QC0/yKxWq1Qq9WYPHlyj3VGRUUhJCSk1/hI42lvgfZDMDk5OdiyZQtmzJjRNSc0NBQWi0WR7fIXcvT3pZdewtatW7FmzRrk5eWhsrISe/fuHfC8nJFisP4C7Z+f4HA4YLPZ0Nra2jVn4sSJ0Ol0yMrKwqZNm7Bx40YsX74cdrsdb7/9NuLi4pCSkqLQlilPjt4uWLAAU6dORV5eHjZv3gxRFPHqq68iIyNjRBwaH4gc/e3cq9Sppqamz/e7QQ37epoRJicnRwLQ61FVVSVJUvvlS30tj4uL63edvES3nRy9feyxx/qck5OTo8g2+RO5fnarqqqkKVOmSDqdTkpISJCKi4t9vi3+aLD+SlL/P591dXVdc3bv3i2lpKRIBoNBslqt0pNPPimdPXvW9xvkR+Tq7ZUrV6QlS5ZIRqNRio6OlnJzc6UbN274foP8jFz97e5eL9FVSVKQnf1EREREAWFEXh1DREREymMIISIiIkUwhBAREZEiGEKIiIhIEQwhREREpAiGECIiIlIEQwgREREpgiGEiIiIFMEQQkRERIpgCCEiIiJFMIQQERGRIhhCiIiISBH/D474YZSoxKgVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We appear to have some populated islands.  Separate out the main state geom in next block.\n",
      "we will scale longitudinal distance by 0.7964\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 3181 is a true island.  We will move it to adjoin the mainland in same county.\n",
      "unit 7374 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 59 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 183 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 190 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 6276 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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HfSm7caoq1956Gz6tzENiNLlxw+23kvznp+xKVQjzMTH0rGuOPEg95s2GO+rhkYR6KAg5dFFXG1LiH4qVpSPPkSSaEPIccVdRoVrSo9HpGx6r3fYnZ+76h/POv/l4pRxTU1coS4cHWY2c1NarnfPsTpYfKGjbSgRBEJpIBCGNuHNXOmU6PXN7RKHpgDwYTquVWB8fpt53H6k7dxDTPZG/5/3Mn7/8RLXFyqVXXU1IZFSTy1NVlZyiEi6++JJWByAHGX2CGXjp/wjbuphda//BLaq3S8ptD97/ul+cFYzGZCQyqn13s21vWosNRBAiCEInIYKQRiiSxEDVzrAAn3ave/cff7J65Qr69e+PrNEQ17MXABPOPR+ALWtX8/Wnn3DjXXfj6d209uVnZ6EqTromNi9vSWNUp5PyA1spykhhw+bN9E6Mc0m5HUcSsyQEQRDamQhCGmGTJCo7aLrMqgXzKddqCW0kYOgzeChOm40PXn8NjSzXjRmoh4Y8fL29mHD2OYRGRjecExQWjoebG68/8yShwcFcdN2NLW6fpaqM115+gUR/icCAAE4bO4HQ3mNbXF5ncJIlfW2xU+RlCoJwkhBBSCPGalUW2SWm/7OOG3rGMdjfu93qvuzpp9k8axY/fPYZ0x99FKPZfNQx/UeOov/IUTidTlRVRaut+1aqqsrieT8z95uvcSgK02+7Aw8vbyRJ4s6HHsFSXc1n77zFivl/MGLC5Ba1z2DyIMqtloGnnUNor1Gteq2dh3pK5TLvXNPRBUE4VZ1kayvbz6vD+nCNl4nf0fPKpl3tWrfB3Z0h06ejlWUWffzxcY/VaDQNAQjUTXg8fdo53DbzYRLi4pj9+WfM/3EWn7/5Gt998C7VVVUMGDyYJcuWt7h9ZenJWJ0ywQkDW1xGZ6SKbgJBEIR2JXpCGuGt0/LogER+XLCO7j7tn5DrwOrVAJx24YUtLuP0aecyf85snE6FgcNHUFpUxKfvvwuSxFXXTm92efs2LSdl3UJKKqrp37c3st6txW3rnEQUIgiC0J5EEHIcq0srKdIbmBoX3WZ12GtqyNy+nej6SagAJfv38/n8+dx54414hLR8tYZOr2fqJZcd8diICZNaVJatppIlf87h9AmTiOg95ojlrf8NYnxCEAShvYkg5Dg+3JpChM3BAK+j52S01hvPPouxshLVbgeg+OdfGBEWRpc+vfnzt98Y7u2NT3S0y+ttqcq8/YSZnUQPmNDRTWkTitN58mV+FQRBOMmJIKQRWyqqmY+OV+NCG00s1RqnJXTnnzWrueGhh3D38cFWU8P6WbNYuXAhZ152GaE9eri8ztbQ4KSotqNb0XYkvRvBub8elgDtv+ngKxP9PoIgdAbio98xOFWV/63bTldbLRdGhbZJHb3OnEqFmxsrPvkEAL2bG8OvuYZLHn+80wUgTmsNf375CkkRvh3dlDbjN2AqxZrI/3QAIgiC0NmIIOQY3tm5j82ynlf6d0crt81FSaPTce+tt7Gmqorl77/fJnW4Qk1RFj+++RC9Bo+h7+VPdHRzBEEQhP8QEYT8y+L8Yl7IK+dGLwND2zg3iHuAP9ecfjrLMjLatJ7WWDb3Y6Kiu5A0qfmraU46ohNEEAShXYk5IfVUVeXrtGwe2p/PaVqVh/t2P+K5n1N/ZkP+BvoF9uPc2HNdNk/Eu0sX7Ho9DofjiHwfnYUkgbefa/aa6fxEFCIIgtCeRE9Ivd8Lyrgvo4gAFD4e0feIYZi/DvzFo6seJbUslcdXP859y+5zWb2eYWF4VFSw++efXVamK5Snb+O3t+7Haqml28jzO7o5giAIwn+QCEIAm6Lw/s59APwyojdmreaI50stpWhlLX0C+gCws3iny+qWJIn+oaHk7mrfrKwn8v5n39Jz2HjOuv0FtDpdRzennfz314wc7MBTOrYZgiAIgBiOQVVVblu1la1Oidm9ogg3Hp2E69zYc0kpTWFF9gqmxUzjoSEPubQNgX36krtpk8vKcxTVUjJnL86SWtz6BeF5RlSTh4/slmqytyzGS2sjqFtfl7Xp5PDfH46R61+jKjaPEQShEzjlg5DXtqcyzy7xQVwYIwN9jnmMUWvkiWFtvDLEhUtDyxccwFlqQR/lSeU/mUhaGc/TI5t0buqKn/l5xXauPX8aRi9/l7VJ6BwOjjKKEEQQhM7glB6O+SunkJcKq7jLx8S0yOCOa4gkobrwsqDxMuCstGHdXw6ALqTpGV8Txl6Ct1yN3emy5pxE/vuXZqm+J0T5779UQRBOAqdsT0iRzcFd29MYq9dwf++4Dm2LqwcBvCZEo/E04Cipxa13AIZor6a3RZbpFRvNT7/8wnVdemDy9GtxO/YXVfPZyv3IksT0EV2I8P2vbXh38pEb5oSIKEQQhI53yvaEzFyzFYDXh/VG/o9lyZS0Mh4jw/CZ1q1ZAchBwy+ZweCkLuyc/3mL26CqKpd/vJaFO/P5fNUBRr74D0qn//j93/o5OJaDv/Cd/lshCMIp4ZTsCfkrp5BfnRrejAshQH+qrPw4MbulmgPr57Nx7UpqHRJ+7jr6t7AsRYXyWjtJoZ7klltc2k6h5Q4G3GJ1jCAIncEpF4QcqLFwT/I+Ruk0XBAV0tHN6TQcVgtvv/g4Q2L8GDn5QsISB7eqPI0s8eYlfXh14R56h3tx/8QE5DZKgS803aGeEBGGCILQ8U65IOTtLbup1mh5e0TvNtkdtyU6sh0b5rxBRlYOhdVORvaMYcA5N7us7LEJQYxNCHJZeW1N46ju6Ca0uYY8IWKJriAIncApFYSoqsraKgt9UTvXMIzqyrUxTVOeuZulv3xBaonCjbfcgdkv9JTeQVZVFDSqo6Ob0eYO5gnp/PNzBEE4FZxSQcgHu/azV2fkie4RHd2Uf2m/C0J5TipbF3xHanYR48+YwFkDJ7db3a1VkbaNqqzdOK01WCtKcfMPwSOsGzWlReya/yOW6mr8QoKJHXMW6N1wD48ne9lsSrIywGnDZlUIMHujN2hRFQVUUFUFyWlF46jE6ghg3Xt/HfpuqCoNHQaH3T6Y6GvA5N6EdAt36WssL6whbUsRXgEmuvT2d3kv2cERMafoCREEoRM4pYKQPeVVAIS6d66lopIktWkvRFVZMZt+/YjCohJyKp2MHNSX88ZfildYTJvV2VSKw07O319gqamhpiCD9AN5OKpK0WvBHBiGp1lPXk4hdksNBjc3vAOD0OgM6M2eFKSvpnrRn2hkhZ6TL8Qc2o28LcvYMPdrJMVORWk5QdHRhMZ1B42BHet2s2r1eiadPQ3/EF8kWa5723VGnBgp8h+Jn84b5Pp1MpIESIcCAanueyVJsG9TFnvX73NpEFJdZmXWM+tRnSoOu0JwV0/Ou3+Ay8qHQz0hIggRBKEzOKWCkEcGJLLqnw1MWb2dX4ckkejRSYKRNrwgqIrC6u9fxqZqmHjx9Zj8wpD1rXvdDquF7R89QG2tla4jp+IRHospNBbFVoPG6P6vg20oNaXk71hL2YHdeIV3Zd3cb9FqZKprrNTUOvFzsxMU2x2/2N7073Ua/j2Go9RWUpG+k8qycrqd0xe9hy9Gn8ATtq1reHe6Tr3xmM9FT4KMG+8m7oIrkTWao54PaMZc3LKCiqYf3ESl+TXYLU4GndmFdb/uJy/N9XVoRE+IIAidyCkVhPjotMwfM4AJi9fz/PpkvhzbuhUgnZ3TUs3iz5+mRjUwZfpMtHrDMY9TrdVUpq6nLDONvF2bkfUm+t/4LJL2yB+P0u1L2TLrPTJzKvDxMhDVsy8pi+dRXlKK3WpBRYMsKaTlK4T6yBj1Ek5FQas34h/ghXdoJKnL57Mvx8aAnoFMef4DJG0jc3M8/TAGRXPisKN5rNUFJC/ZTK+x/TvNxOSDQrt5ETswiHW/7cfdx8DEG3u6vI6Dr9ipiNUxgiB0vFMqCAHw0mkZYDbyi1Wh3O7AS9c53gLVxRfEgh1LeX/230zsFcG4s29Cko/MS5f20xts/G0uWrM3SBIevn54B/jhGz+QjHWLmH372ZjdtEQkdEeSZBYs3EpSjBd9z7ma0b1HIelNLW7baa18ba0x4dYHWfndN6z+8XtQVWSNhvDEPky65ZIOD0pkjcwZ05M4/aruyBqpTdsjnwKJ2QRB6Pw6xxW4nZ3bvSs/btvPipIKpgT5dnRzkNqga7wiP5Pe/k66xfVg4zv3kpeRhaqqVFRa0MrgaVQ4541f0Lp7H3Vut8lXAlBdmE3mP9+D1sitb92AMTDK5e1sb92H9qT70Ocb7lura/ng1tt444o/uePLL5DltkkivHL2X+zbVEtNhYqKjG+wgqwBWVaJH9qNxJE9G+rWaNsukfHBnzRJ5GwRBKETOCWDkLUZObjbbYz2b35K87bQFqPzes9gktPWY8x9l/gRZ9DzkruRJBlZq6Uq7wBesQOP6h35N3NAGAkX3tMGres8bLVW7LX5hMSNa5MAxFpjZe0v69i2RCZ+sJlRl4yiprKGkpwSnDYnZQXlLJ+VxtLvMjC4WYjq4cX4a89weTsOOrgyVyN6QgRB6AROySCk3O6gSqfnh/3ZXNE1Am2n+FTo2jZEDhhH35Rctu3dw/Z16UQXLGTKhZegN5nw9jp5Eoi1NQ9/b3qMvYbU9X836zydQUvyP6V0G5BFaOyxV8jsWrmbxV/twTfYwbir40gY2gMAvcmAd6BPw3EDpw4EoLK4gllPz+ftG//E5F7FqEt60W1AfAtf2bEd3LhOZK8VBKEzOCWDkJl9u5O7cgszM0v4NSOPl4f0pqvbsSdttgepjRYqTLnsCqYAVouFT994lb9/mcvUiy9rm8pOUoqikLLqNy577oVmnTfyotNQlWX88e5qHDYNYfEGVAX6ntGDiMQoaqtq+OfrXfQa48PIi0Y3qUwPP0+ue+0CFEVh9dxVzP84mV0r93Hmna7L5dLQE9LJJuUKgnBqOiWDEC+dli9GD2BxXhH3bE1l5OodXOym4YZe8cSbjR3dPJczGI10i4ujIC+vo5vSKTgcDua98gm1lVWU56cTHNsfn2D/Zpdz2iWncdolkLphD5XFFWTuyueX17cR3GUjxTkKbh7OJgcgh5NlmeHnjyBxRAmznv6Hd2/+mfPuH0xQl9bvddQwJ0QEIYIgdAKnZBBy0Nhgf1YF+PJJygHeyirim3W7GaXaeGVYH8KN+nZsiYraxteE7IwMvHx8TnzgKaAoo4D9m35lyl3P0aVPPAZT677X3QbEAdB3AuSmZpO1O4seozyIG9y6oRSfYF9uevs8/vpgPrOf20BglIMLHzqnVWU2DMeIIEQQhE6g7abhnyRMGpnbEruybdwA3o4JZo9NYfLSTczJLqTM3j57ibTH5SCzsBi73d4ONR1bSU41c17cyBf/W8mGPw50WDsAqmvKAVj0Qybb1ux1adkh3cIYOHUwCcMSj5kQrSUm3jiBa14YQ2Gmlg9u/5E1P69sSB3fXAdP04g5IYIgdAKndE/I4QyyzPmRwYwM8uO65Zu4dU82mt2Z9HNauahrOJfFhJ/UXdg33HwzH3/0YYfVv2puKrWVNrwD3Vg7Lw03Tz2JI0LbtM7qykoy9uxGVVUcDgfbli4mf9MaZE8f+l10FXa7H2vnZCLJEgNGJbZpW1rL7OPOzW9PZuNfG9i6KI0Nf+RjcncCEk4HBEYbOOuOycja4wc+B1OUiTwhgiB0Bq0KQp5//nlmzpzJnXfeyeuvvw6AxWLhnnvu4fvvv8dqtTJhwgTeffddgoJOjhUZQQYdv44bTI7FxoLsAn7fn8W9mcVsLy7j+cGuz2DZoI0DHE8fH+zIKIrSZrkwjsfsbSArpZTaqrreGO+gtk+Z/8FdN2L2C8Ds648EePj7M/6V9wgKP7SBoXfADtZ8l9PpgxAAWath4NTBDJxal+lXVVVQVUrzS5n97EI+f3AOQdEmptx2ZqNlKOrB4Zh2abIgCMJxtTgIWb9+PR988AG9evU64vG7776b33//ndmzZ+Pl5cVtt93Gueeey8qVK1vd2PYUatRzdUw4V8eE88buAzyXW8bNtVaiTG2wiqYd9vGQkNCjsnLBX4yc2P475464MBavABNVpVbiBwcT1MWzTetb/dfvSJLEjS++0egxpUUV7FydhWxumxTmqqJg3ZuKxtsbXZCrE9Af2vjQN8SPa185j5ryKn54evFxzznUEyIIgtDxWvS3qKqqissuu4yPPvoIn8MmO5aXl/PJJ5/w6quvMnbsWPr3789nn33GqlWrWLNmjcsa3d5Gh9VdQKavTubPrPwWj8c3RqJtEpYdzujmxmlDh5CasruNazo2nV5DvwlRnHZxXJsHIP/M+YFVn73HWXc/eNzj5r5dFxhf/cQYl7dBdTjIuPoa9k+bRuqoURS+/Y7L6zicTq/D3ccDm1VDWX55o8cdXKIrJqYKgtAZtKgn5NZbb2XKlCmMGzeOp59+uuHxjRs3YrfbGTduXMNjCQkJREZGsnr1aoYMGXJUWVarFavV2nC/osL1O4e2Vi93E9e4aVhSZuGavbmMXr6fxzbvx92hHGNWafP/uJdhQQ1u+V4sTaXX6ymrqOywIZm25HQ6cTgcaLVaNv82lzG3zCAmqfHhs4qyKmrzdFz/+mkY2mAllHXfPmrWrcP/lpspevc9it5+m4DbbnV5PYfTaLUkDvfi60dXctYdiUQmRR91zMHhGBGCCILQGTQ7CPn+++/ZtGkT69evP+q5vLw89Ho93t7eRzweFBREXiM5Kp577jmeeOKJ5jajXUmSxHODe6KqKr/ll3G3ks5lExL5oHcXBnj/a+t6qdE7x6SqKprd6az76Rc+f/GDo55XKu1IBg2S3gVBg6pS7lRZ/uUsRl19SevL6yRSd2znj3deRavXo6oqPlFd6TV85HHPMboZQAKno22GYvRdumBISKDo3fcA8Jo2rU3q+bcxV4zBaF7Fr2+lcdadENE9+ojnG4ZjRE+IIAidQLOCkMzMTO68804WLlyI0eiapF4zZ85kxowZDfcrKiqIiIg4zhkdR5Ikzgz2oaeniRvW7+OKrWnMGxxPnHvLezEkJEJ7dOGWHne5rqHHUZ6Vz0+PPsrAKRNxCzj584Yc2L2T3995hQvue5jQLjFNPq+6ohZJldEb2maBmKzXEz3re2o3bkTj548xPq5N6jmWoecOo7bqb355bRshMduZdvcktPq6VTOiJ0QQhM6kWR+vN27cSEFBAf369UOr1aLValm6dClvvvkmWq2WoKAgbDYbZWVlR5yXn59PcHDwMcs0GAx4enoe8a+zi3Yz8sOweHxlDVPXpvDdgYKOblKTOGx2dvz6F8XV2ThtHZczxJWWzP6ekZdc3awABCBlU2bdjTbsEZANBszDhrVrAHLQ2CvHMf2VcRTn1LJj2Y6Gxx31QYhO9IQIgtAJNOtj4Omnn05ycvIRj11zzTUkJCTwwAMPEBERgU6nY9GiRZx33nkApKSkkJGRwdChQ13X6k7AW6dl3vDu3Ls5jQfScujv79GqHpH2ULInnZyU3dz2wQ8YvNt+iWxzbVm5nLW/zsXDL6DhsUOTgNWDDwCQtzMZnbsHXkEh9BoyrNl1ORUHivTfCMQaY/JwIzDKyJ51B+g9rm4Vm7Nh75gObJggCEK9ZgUhHh4e9OjR44jHzGYzfn5+DY9Pnz6dGTNm4Ovri6enJ7fffjtDhw495qTUk52/Xst7A7oxZtl2Llq7h4fiw5gS4otJ0zknfWZs2EJgRNdOGYBUlJXxz6fv02vimQRGRCBJR76HknwovZYky5jPuYDQLjFodboW1Td8Ym+y9hby/h2LcA9TOfumYXj7ebTyVXQ+VSU2eoyKbrh/sCdEK3pCBEHoBFw+IP7aa68hyzLnnXfeEcnK/qtMGplZg+OYueUAt+3N4vm9Ofw+rDtBhpZdHNuSqipUV5R2dDOOadnPP+IRHMLpF1zcbnVedPs4aqpqWfTjJr55fBUTb04kJrFzzkdqKUnSEBxzKFGgGI4RBKEzkVRXJ71opYqKCry8vCgvLz8p5occbldVLRPWpTAjLIC74sM6ujlHUVWVz6ZPp1u8CVmW6tPQHznMccz76mGP/1v9xUyqv93wVQLKs5CMnmD0qTtAkusTbMkNibYkSQZJIjevhD0puZx9RihRl72G1mh29cs/rtULk9n4axY9xwUxcmqf/8wS5q8e+oWJNw4gILLu5zG5sobxG/Ywf0AcvT06X4+YIAgnr5Zcv8XeMS7U3d3EcL2BRXllnTIIkSSJ8d2GYuhvArMZVVHh4MVWkqiPFOoDBOruH3y84ZPzYZ+g1fo9WevTh6v1wUpDXFtTgqo1gMaIqiqgKHVfD/unKnVfA2NUohOL6bLrKYrSriIwcXh7vCUNho7viX+IF/Pf38n2xX9x8cxh+AV5t6gsR62F/V/Mo3rxYmJeewaPMD/XNraJFEWhqkzBw/fQKihH/bdGDMcIgtAZiCDExWLMRn4p7XwJ1w4y62UcK1T8rh+AJqzzDT1UpJ+B/cfbSdvYG31Yb0JGXo5G1wap8o8htkck4S8EMu/j1Xzz1EpCexs4//pxJz7xMClvf0/l3B/RDRuN9zlnsfvS6RAVT9Jz9+AW5vrU7Y2prarhz/cWoyoSerdDy+md9QGimJgqCEJnIIZjXOz3vFKm70rnLDczbw2MwdDJuvVVh5OKj75G42PEfNGFnXJnYFVROPDDQ3TZ/S6Z4WcTcd0Xxz9eVV3+OvalHGDhV9vQ6/Rc+9jEJp+3YcKl9Jn3Bdr6OUHW8ipSnnkf+5pl0L0vTosN7+GDCD9rFG7BvtQUlFCRvLeuJ0lRAQXVCQd7lFSnguJUkTiytwmoz8F+7F/fzJ0ZZO7KJml4AGZvc32Pk8qyXXuZOeFCVgxOoJuba3L9CIIggBiO6RQmB3nzstXOg/tyCE3O5PHeUR3dpCNIWg2mEb0p+3E7piorGo/2uxDZnQpP/baTJSmF9Az34tmze+LldvQEXkmWiTr3EbLm2iBnS6PlqU6Fkh/2UJtciNbfDb/LEtAFuWYuSUx8NDFPR/P9G4t4997fOe2iBHoMPH4ukoq0bAxZuxoCEACDlzu9XryX3H9GU5VZiF/3CPL+XMbOK25C328wtk3rcYZ3Q+duqguk5IPDX3XDZwdTi2m08mFDY/Wv/7ChMgkOzeORwM1hJyaoEiWrispsGubhaALqJqmKiamCIHQGoiekjdy+PpVVldWsH9OrU6bILn3tYzwuORNtcNCJD3aRnzZncfesrVw9LJrPVx1AliDtuSlHHZe37ieC/7iaPEM3AmYsR2M49gTK2t0lFH++A8/xUVQsTAcg/Pnjp2tvifWLd7H2hxwCEiXOv2UUGq3miOdVVSXz1+WUPPkoptsfJP6qE/ecKA4nu9/8DnPXCKLOHuXyNjdmaUklF23dx4ahiYS3wZ45giCcukRPSCcyLMyH2SlVVDiceOs639ss6bSoleXQjkGIn7lubse6/SUADIj2PeZxPj3GUfWHG8qgGxoNQAB0/ibQSlQuzwJA30a78w4c250uScHMfnENv3+1ikHjuhMc4Y+lppwta57CsWol7vtPI2buLDwim/Z+yloNiTMub/T5otoi9Bo9nnrXvia7yBMiCEIn0vmujv8R9etGyLPZO2UQog32xr57F7rY9kspflpcAG9f2pelKYVcMiiCywYfe6iqJnsnXtRiHHHlccvT+psIuq0vtTuK0QaaMPXwb4tmA+Af5MOkG3qzcVkKs5/bSNSQFRi8tuMuQWDXIcQ9/ILL6npq9VP8sOcHAO7sdyfX9bzOZWWLiamCIHQmne/q+B/Rqz4Hw6KcUhJiO186d2etQunseRR/9OyhB1VAAq2HkZBvlrZJvVN7hTK1V+hxjylf/jGFSTOIM5z4fdMFm9EFt09OEb9QDX6xf6ENXIlZP5wxE+aw6aeJhPR0XYK1UkspP+z5gYvjL+b7lO95Y9MbLg1CRMZUQRA6ExGEtJGeHm4M1On5PquYW2OPf9HtCJZiGfcLp+Nz3pFzKFRFIevsjt3nx1S0BVVx4LDWYi/ch9NWg9Nhx2G34R6ehMGr/Za6HpSRsYY9ey8HdTTDhn2CtyGIbe9OBm8n7kkDXVaPl8GLHn49+D7lewBCzCEuKxtExlRBEDoXEYS0kfRaK1tsNm4PbrshghOxF5dT8vUSnMW1oJeRZAlHkbPhea+pXY86R6pfUtwWy16bynDx5yjfT6f8lQGUeiahag1IsoZuOT+RNexZws+4tU3qdTqdLFl6NzZbLm6mGKprFoPqjyTnAgb8fO9i0KDbAKjYtIzixL30Dfqo4T1rDlVVyZ77MuWzfgJU8DAg2UEO9uWd2x9nk1KASWtiaKhrA8JDG9iJIEQQhI4ngpA2UGizM3X1bnyRuDXetZ9km8qankfBW9uR3apwH9YNjYcbqt2B6lBwH90H+V8rPA4n6zWo5UVI3gGNHtOWvCOT8L5/DQAHc43aKwope3tNmwUgAFu2fIXVmkFw8DQcjhpiYz8mNLQXlZX5KAp4eR2adOrZ7zTil93D/k3P4Zs0tln1VKVv48CM69DFRBL7wS/ovP2xlRWATqZ8yyJy7prOkFc/wT2sl6tfohiOEQShUxFBSBtYkF9GoaTy94A4zJrGL/ZtyZ5ViKQtJOzJy5p9rmQ0olSUIXdQEHIsOs8Aqp1avNuo/PUbPqSi4gUS4j+ha9fRRzzn4XHsFS8hw6aTlf5Vk8pXVZWafdvJ+f4VLEs24HPrNYSfc0/D8wafujoCR1yMPS+H9AdvIeqJN3ELT0DSakGnc0nPlN1R1xMmJqYKgtAZiCCkDczZX0gwMj06cIOw0rk70AU1Pw+EqqrYC8rQBHSueSy1laX4Ut5m5eflfkH//j8RGtr03geN1gCScsRjTlstlanrqNq/merN63DsykA1a5CKreDvhvuYMUTPeBOdW+NLb0POvQu0Gg68NgOsCpJTqRtHqZ84fExNzPaTmTACefhFnTJ3jSAIpx4RhLSB3U47IXLH9IAcJGlVnFX2Fp0rG3VIpnZYcTLvTtgxB7T/WgWj1cOdyYc21wPyV36LtvsVhLdBMzZu+hokBxpN0+Z2OJ0WrNZ8bLZiHI4Kto/vhdPNgdbsg1ptQRMehLFbLF59x+I9/XR0Jh9kDw+kJvaKybJM2Nl3Enb2na15Wce0Kj0TTVqBy8sVBEFoCRGEuJiqqkgqDPVq363o/y3k4WnkPLaU2uR9mHoeP934QWnDE7EWqxj9HPBSbBu17OBHdgns1XDOh9B96pGHPO4FT/qAObDhnLDaSsoumOv61qgqeXlfM3TI9/j7dznusfkFf7Jr1wOAhEZjxOm0IHtpME08jZh73nZ529qC3WlBI3WqJMmCIJzCRBDiYsV2J8WSiq9nx24OpvFww62vlsIPVxP5VtOCkIhv5pJ729Xog3zgvj/buIXH8fiRwy6O2goqXuqHZ1iCS4p3Oh04HBWkpi7iQPpHeLgPP24AYrMVkZPzI2n7XyMp8VWCgo5ONX+ysDstdK4tFQVBOJWJIMTFfHUaukgavj5QyIyuLV8Zo6oqK77/kuRF83H382fizXcRGH30ktrjtuXycVRv/I2yeWvwPmvICY+3rP0H1ekk6G3X9zi0RuG2v6ny64+fp9+JD66Xlrac6poi/P2SUFUHublrqKg8gMXyJ06nD2DAaIhm+LAP8faObrScvanPk539DZKkpW+fL/HxGdz6F1SvylpOVtl2UoqTUVQniuoEVUXBWb9jrhOVg1+V+sfq5qDYnZU47BUYjCGAxLG2gFLre50Of2pTTTAqrstrIgiC0BoiCHExWZK4s2swd+3LJqXaQry5ZT0ihen7WffzbHqfMYWtC37nqwfu4J5ZvzWrDEmS8BhhxrI7H8468fFaf38kFFCsQOfI8qo6HVgefYIKiwdr5k87/JnDbktH3NdXHaDkZjsOz8Hk5c2pW17r2QNvrzg8I8cQ03XMCeu128spLVtPZuan9O79OX6+w1zyehRF4Y/d7/FH2q9U2asIMHoT590FnUaPhIyEhCTJSJIMkowsyYBc95wkg6xDRkKjGpG0TrSysWGyav1eug1fD74zdTfqbqlWdxxOMRwjCELnIIKQNjApxIcHUrOZufUAc4bGt2hppZuXN1qDgdT1qwHQ6g0taoupVyyVS1ZR9usqvM88/oXUdPoF+BQVkHHuWEzd4/B/8l1kz2NvMtceVEUh95pJePTrR5fnPmvyedtnXYnkt5KJYz9vUb1Z2d+SkvIY7u4JxMc/3awARFVVdu3aRU5ODjExMXTpcmiYJznnb15d9yRx3jHcN+RpuvgPaFH7WiMrLZ1/0sXEVEEQOgcRhLQBL52Wh7qG8Oj+XJaVVDLKr/k7obr7+HLJky+xe+VSPPz86Xn6ibeHPxZjXASym52qVeknDEIAPC66FffzbqT8zZlknDOGwLtuwzRlOqhqk1d3uIozczfO8mr8v2x6AAKg2qyY8wNRnA5kTfN/xLOzvyMwcCI9e7zV7HO3bNnCL7/8goeHBytWrGD8+PHowgv4ZsdHOBQHDw1/lW4B7R98HKTXyChiVoggCJ2ECELayPVRgbyQlsuCnNIWBSEAgdFdmz0P5N/yX/sZ1eFG0J0jT3xwPUmrxXvGS5iGjSX7/vvRvPUekl6LJEH1/moCpvbEmlWAx+nj8Ljmf61q3/FoI7tTtacER84BtKHRTT4vesSDrNt/Po7qEvSezd9nprY2g4EDfmn2eQDFxcUYDAYGDhzI/PVf8fyB2wkpDeW2fjPoFTquRWW6kkHSoDSabEQQBKF9iSCkjUiSxEQPdz4pKiUpw41LI9s3+2jVyq1Ur0vFliER9uxkZFPz56YYhkyi67JJDfdVRaFm/hwkWcUjPIq8+2+lZs1qAp77CNk3uNnl2xwKz/y+k1X7iukX6cNjZyXipj/0I6lYakCVUIpyoAlBiKqqrM5dTWHVftwBa0V+QxBSVVVFUVERISEhGAzHH9rSaIzILdgPBmDw4MHsylnF7NSn2RaUx6fjviAmqG+LymoLBo0GFRmnqor9YwRB6HAiCGlDr/ePIXtdCvekZpPkY6Z3O2ZQLf1xN4Y4TwJu7dmiAORYJFnGPOmChvsRP69m78AemP/4AvfLH2h2eXM3ZfH12gwuGhjBt2szWLa3kNUzTz9Un9ENtygT5V+9Q8BLJx5Ken/r+7y79V0A7i3REGj5jp7hPTlw4ABff/01DocDgJtuuong4OYHTSdSWVvIs6uuI9u4hwsSb+ThyMkEeca5vJ7W0Ncn0bMrKhqRu10QhA4mBofbkE6W+KhfDKoEEzbswaG0z6qEmu0HQDYTePMkjN3aIsdonYovXgFFRRffr0Xne5p0OBWVXbkVAHQLdD/ieUmSUJ0KRb9uwlmUf8Ly1uatJd4nnjO7nskCtBQYZwGwfft29Ho9Z51Vt0To559/blF7G6MoCn/v/ZLpv5/JiIiJfHHBTs7scVenC0AADPVBiOMYS3oFQRDamwhC2liAXsdPfeqShf2WV9IuddozC5HkyjavR98lDre4YAwDx7fo/Ek9gnn+3J5E+Ljx+JmJfHHNoKOOiVqwCd/TE8i58fwTlndtj2vJrsrm17RfifWt2wOmdOdievTogc1mY968eQCcc845LWpvY/YUruLJtS/xzsTvmJJ4s0s2mmsrBrmubdZ2CogFQRCOR1KPleWoA1VUVODl5UV5eTmeni2b0NkZTVy6Aw3w+6ikNq+r8KM/qd3pxPeCaNyH9Wi7ilSV3T0TSNie0nZ11NudlIjXwAhkgw7/p99DExBxzONsThs19hr05UVsWXQhvcd+gzm4O9XV1Q1zQvT6xjf2U1WVFSuHMHLE2ia3bcTXvRkf0p3HTv++2a+rrWw6sImP1n+ETtIBdb1KkiyRqwtnqXkcW4YlEWzQdXArBUH4L2nJ9VvMCWknV3YNZEZqNuV2B166tn3bA66fRNHn8yn5dkeLghCb04ZTdWL698Zy/6I6rOi99aQOTCDqs0/R9XBNQq9jid+WjCNlA6Ufv0bx03cT+MaPxzxOr9Gj1+hJ++dN/NxGYA7uDoDZbMZsPvZ+Ptuy57Mz6ycclRvR4MBTqmFX/kriA4Yg1w9fVFlLmLPtRYZEnUl84HCgLmC55bfx9PMJafMAJLsoG7tix6A3YNQbMegM6GQdGlmDLMlYrBbW7V1Hjb2GlRkr+bnsZ64MvZLTY09HUZSGf1tqYWkJWBXlxJUKgiC0MRGEtBOlvvu7wqng1Q4fQP2vnkDm9h/I/t/XBD94LsgSGvcTZ0H9ae9PPLv2WSxOC9f1vI47+zW+k6ukM9J1RTKWtQvJf+ohrOkFRHzwHvrep7nypdTVpdGgSxxMwAtfsrtXLzyXzMU4+txGjw8bdjMrNg7Bc+0AQgZfecRzDlsVsmwktzKFRVvuQCPriQk9m159nsOhOJm/62Xe2vAMWdWFhBg9qXJYkSWJnn6J3LX4DpI8/Yn07MruslQi3MP435gvjtv2ipoK9hTs4Zftv5BSmsJVva5iUs9JTV6BM2v9LL7Y+QVBhiCcihO7045TdaLU/6eiYlNthJpDCTIFEeARwJ8j/iTc7xjzgcqqoCSVCoezSXULgiC0JTEc0w7SaqxcsGY3iUYjXw2Lb7d6FUWh8PW/sGUpoPPA79Jw3HoffzO7sT+MJd43npSSFAprC5l39jy6eB1/d9mD9vSJp8uc2ehiermi+cekqio5F42iYlshccsWoglsfOJt8reXoDV4EjvpJcpTl5O9/XOqpDQ0NgOqRqG2oAyv856hX9R5xzzf5rRSVpOD2eCLWe8FgFNxkFK4nr1Fm+geMIi4wMb3YbHZbXy55ktmp84mwhSBWTJTaa/EITmoVWpxOpzMveb4+/RU1FQw7ftpfD3ta8L8wprwDh1foc3OkBXJKMCdkYHc3DUUQwuXIwuCIByuJddvEYS0sfXl1Zy5aS8AawYnEO3WMbvrFrzzK0q1neD7G+89ALhnyT0sSF/QcH/tpWtx0zVtaXHJkzdQvXYDQS+8jb6NhmbK33mY0h9+ImrxthNmcC1Y+z3797yNU67FLEURHHsB/v3OQ6OpmxPyxwUjmTx7ucva5nA6GPrVUEK1oXhIHpRYS3DKTl46/SV6RRwZmO0v3M+jfz+KTtJxbsK5dPHrQlLEkfOF1u9dz/2L7+fWgbdyfr8TT8xtqiKbg8e372VumYUAGZ5IiGJakE+nnlArCELnJ4KQTqjc7iB+xXa8ZZmtI3t02KdOS2oGRR+n49bPie+Foxs9zu6083fG31gcFsZHjcdd797osUfVsS+Lord/wpqWDoDvaeFojFqQ1LoLnCTV7ahW/7XhotfwOIeOk+sfkI88rmbDGmoLIeiZp9BHBB23PZWZW1i39zz6Bn+Ab2JdttKS7DRWvzoTu+rEd+M+Tlu6ucmv79+sNisbD2xkS+4WlmQvocZeg4/WhyfHP4lGoyG/Mp8eIT0wGRofBluxZwV/7P6DA5UHKLAWcHe/u9mUvYmtxVuxY+epMU8dFcC4SkplDfdtSWGdQyKprJgXRg9igI9Hm9QlCMJ/nwhCOqmXdmbySn4x3SUtHwyIIa4JczPaQtXq7ZT+sB+kSrT+OoIePK/FmUGPJXPG75gHm9EFeaNaqtFHhiFptHV7ySsKqAqqooCigqrUP6Ye9tjBx9VDx6rqoX8KKNZayv7JBU043md64T688Qu0oijkrfyE0JHXNzy2c+EPpP30DX1uehCzdwA+kd2O/6IO1v2v90lVVUZ8MoI+Xn3o4tOFyd0nkxiW2Kr3b1vGNj5e/zHxvvFcMugSfM3ts3ng78m7eXBfDoVevkyRHTw2qCeRppZtmCgIwqlLBCGd2Oayam7avI9MVeHuID/uSzr2EtO25qy24SiuoOCNxSB5I2mL8T4zEffhvVtddtGXf1OzuYqwF6ai0bfdnOeiT+dTs6kUj9HB1GzKxH1kDF6Tmjb8s/3Pb0nbspSzZn5w4oNT/oR5t4O1EobfBWNmNjw1e8NsntzxJBsu3YBBd3JfsJ3V1Tzx0kuETTyLt8vtlGu0XOtjYkbveDy17btpoSAIJy+xRLcT6+ttZvHIJB7fkcHr+cXYVJWb4kLxc+HFOr2gkrk7c9FIEvOqq/FXjzPGPzYeVVFRanzJK3aQ+MkPPH7mWEIC/Vtcv+fYXlh2ppD39Fr0kZ44K2zIZi1ufQJxHxzS4nL/ze+aM9CFrqNqZTraQE8qlzpBXYvX5MEnPFdxOpq+G/DCx8CvG9SUwNLnoetoiBpKdnE2P+z6gbP8zzr5A5CaGr564kmGhocxvk8il+t0vJW8l/cLq5i1eAP3dgnhypgIdLKYLyIIguuJIKQdmbUanu4ZhSk5g7cLSvi4oISPe3flNB+P4/6RzyisYmtWGYqi4lTV+pELFaeiogCKWvd1eUU1wbKGMSHenBbhS5+ufk1ql81m54e/S7ll4UoGa+GOKafj5t70uSAH6cMDMfVIpnZbGcb4KHTBZhyFtZT/dQB9pCf6kGPn6WguSZLwmjgYr4l1QUfuc3OwpmlRaizIJ5j4qzYnCAnuCTt+ArV+OatfDLkVuUz8bSKTfCbx9OSnW/MyOpzTZuOLxx8nLCyMCXfcAdT9QXiwTwJXWW08s247D2cW88n+HB7vHcf4QDF5VRAE1xLDMR0ktcbCuWtSKJBUQpF5Ij6cIWYT6zNLkQAZCRnQSDA7o4hAnZZoNwOyJCHLEhpAkiVkCTRS3bGyJDG0ix9BPi3bKE9RFF7/6U/yaiy8eMWxl60eT+EHP1O7oxLPcV3xPmt4w+M1yYWU/ZpGyMxBbXIRq9maStmcTTjKJSLfuIDKJZupWrkH73P6Y0o8cs7H5jkfkpO5iyl3vXbigu0W2DYLrBXQ8wLwCGbpzqW8vOFlfrn8l2bPp1Fq7Niyq9AFuaHx7NgeFGtNDT+/+CKqJHPxY482elxyRTWPrNnGGp2Jodh5sn8SPT3bbyNGQRBOHmJOyEmm1qmwurSSB7ZnkKk6cXeqXK8Y8DboUajv5QBU4PK+Efh6tv3yXqeicPHcBcTZLTx98bQmBw3W9DwK3tiEebAbvheNPur5vNc24nFaOOb+x1/R0lKqqpI9c0XDfV1gKbZMO2EvTkM+LEPtny/fgWLQMeX2V1pUx8QvJvLBmR8Q7RfdrHMdxbUUvLMFpaZuJ1+fi+Ix9w1sdhtay26zsfKbb9izezdJkVEMvf465OOksYe6170gu4DHk/dxwGDifLOWh/okiLTvgiAcQQQhJymHonLBllRWl1fjp9NwU0Qg08MDcNN0zHJeu93O6b/8w4yYUM7ue+K07/a8YvJe/BvfS3tjHpBwzGNsuVUUvruVgFv6uGxY5t9qNqdQ/tcOgu45C2tpIXn3/0iN42A6dQlJkqnJLGbt7W5ofLyQZD06jRs6rRsGrQd6nRcmgy9uen88jIF4GAPxMoXiafBHp9Hx5aov2ZSzmjcueL/Zbatak0PZz/vwOT+O0h/3ABD+/EgXvvrjczqdrJs9m21bttArNIxB101H49a8Hg27ovL57jReySzEotFyS6gft8ZHYm7q8JYgCP9pIgg5iamqyuqyKp7al8vmyhqC9Tr+HhiPfxuuMjme4sJibpu/DJukQe908MG08Xh61eWQUOwOarenYUk+gDW9DGcJeJ8Tj8dpx19hU/T1Thx51QTe2Q9Z13YXruqCDPK/W4HH2GgCetatmlEUO4piRVEsKIoNh7MWq72SSmsJVbYyqm2l1FpLqLUVY7WXYbdX4HRWojirQbGgqirW2grCjTYGDliEj3dks9rkKLNS8O4WlAobAH5XJ2FKaNsluLU11Xz+7tvkV9UCMMrNjeE33ojey6tV5VY4nLyyPpkPa+zosfBobDRXR4SjEfNFBOGUJoKQ/4h1ZVWct2UfHlqZOX260b2D8opAXXD0+fwlLE8r4aksC4pFBklCY5bQhXtiSgrH1DcWTRMywTpr7BS+txXZrMN/ek9kXdv09OQu/5vavblEXXkxGq1rhwxSUhaQnvE1Z4z/stnnKjYnjvwatP4mZFPbB5fbVq9k6ZJ/uPKa65gz4xau/fb4KeKb4+/Zr/Bp0Sqcgx5maYVEiLaWV7snMMbfx2V1CIJwchFByH/IhrJqLktOQwb+GdSx4+9lVVYuXZjM3B7B6GNCW5XgTLE5KfpiB7b95YQ+PgxZ37oeEUVxcmDrJpx2O136DECr16OqKrmL/8KSXAZmhZBzT8fkF9yqeg73+x8X0qf3DMLChrisTFdRVZWK8jKy9+1jzs8/c/655xIbH8+PN1/LxV/MOuH5+dX5HKg4QJJfUqPZcp2Kk3PfGMhn1/yOr3cI68squWvnTvZZDfR1s/FGUs8OS8gnCELHEXlC/kMGeJv5o18sZ2zcw8i1u/h7YDxRHZTF8qeduXQ1mzDGNr5ZXFPJeg2B1/ci68HlFLy3lcDb+iC3Yu7LX+++zq7l/zTcv/OruWj1ekJPn0RtnxxKtm6haPVqPAfG4eGfgHzY/AVVVSkoKECSJAICApo8CbdP70dJ3v4/wsLmtbjdbeGHjz9gb2Y2Zp0GTw8PzjnrTLr37YejpoamvLLVOau5ddGt2BU7ALPPnE2C79FzfLIzdhCseuLrXZf7ZaC3ByuGDmJOXiGP7Elj1LpdTPPX8XRC9w4bThQE4eQgts/sxGLMRhYNjMeuqgxZs4sf80o6pB1flZUzvatrV7WEPj4UjbuO8t/TWlXOgS0b6dp/EG5e3gBUlR56j0x+oQQNG4U9p4bs737nwAdH9gT8+uuvvPfee7z77rt8/vnnKIrSpDrDwnqAGsC+ffNb1XZXqa6q5OevPmf3/nQeeOQR7nr4Ma69cwY9BtblUVFR65ZYncCC9AV46D14YtgTADy1+qljHufh6U85tXwz5/GGxyRJ4vyQQLaMHMTdUd78UWyh38rNvJCahsXZtPdVEIRTjwhCOrlok4Gtw5IIMei4Y1cGd+xKp9TuaNc2vJEQyTnpmby9ch85hVUuKVM2avG5MI7arYWtKmfwOReyf/MGasrLSBo9Dq/AI4MlrdFM1xsuw3/QECTfQ1diRVHYvHkzPXv2BCA9Pb1Z9Q4Y8BApe9467jGW2lqW/D6PV596gg9eeZFFv/zEwrk/Ul5W2qy6/s1ht1OYk01JQT6/ffsV77z0IrJGw13334/2WHNgFLVJPSFTukyhxl7DY6seA+DpEcdOxubjG8q3ty3nrwN/UV1TfsRzBlnm/piubB7elyl+Bl7PKKPPivX8kFNAJxv5FQShExB9pScBL52W+QPieHpfLrPzSpibX8rMLqHcEtn0IYTW6Bnpw3oPA6vSirlpXSo3B/tzes8Q9K2cz6H1MCB76KnZVohbr4AjnlMUhYwNeynamoXWISFXKqh2BVUGKciAxtOAWu1Aa/FmdI+b0TllfGOijvl+qKpK1ZZMtF0O5cOQZZlevXqxdetWAEJDQ5vVdn//ruh1Iezc9TuR4SPRaLWkJidTVVmOoqisXrWKKlUi2s+bPn36IAEV5eVkZmeTvD0ZkIiPjWXU1DNx9zj22KnD4SA9NZW0ncnsTd6GojdisduRJQmDXoeqqAQGBnD1TTcTGNJ4+5t66R8QPIAF5y8gozKDeJ94jNrGJxvLej039LyB6Z9O5usb/0GrOzLXiJ9ey3u9ejGjupY7tydzR0oOb+w/wOtJiQz0bn42XkEQ/pvExNSTTKHNzgMpWfxRVI5ZIzPC250bIgIY5u3eLgFJfkkNIzakEOaQWDK5T6vLK9qcRcbcrWiMOnQ2Ge1gP8yh3hQs2Its0hIwJBqNux5TgDue/t7Yqi3kbc/AXmYBk4zspiM8qQt5mw9Q80828Q+NOaJ8W3UFme/9joyBqLunHTUnJDc3F0mSCA4Obvb7V1Z2gEULryNtxzB0Ghlvb29qqqvxcHcnqlssA0eOwmQ+dk6UyvIylv75O3v27EUCDHodWp2OmtpaFKXuV1IFPN1M+AUE4OftTcLAwQSFhjXans3p6SzKKcJHK+Oj05BvsXGgpJQdFjteuTl8c+t1zXp9TfHwe+ejVzU8esvxJ70uKS5lxs5d5DjcGOkJryR2Fzv1CsJ/jFgdcwpZW1bF59lFLC6ppNzhJFCv5eW4CMb7e7Z5MLIzt5yLtqXxZlQoYxJaN1dk428rsVqsDDt/LLYaK7u+W4NqcRJ+Rnf8Y5u+6V1pZiEH5m6l753jjnjcabWQ/t5PKEE1mCPCCRkxoVXt/be/5o5i4rlLW1WG1WKhrKQYS3U1gaFhRwUuiqIwd9t2UnPzGBAWArIGu8PJpspq/q5xoq2pokhvorutmhEeBqqdKmUqeGlkwnx9OCOmC7f9/g+zLju7Ve08lvQ9G7h2wXQW3bb1hMcqqsrnmZk8sy8HCzquCvHgf91icBc79QrCf4JYHXMKGeztzmBvd1RVZUlJJfemZHLl9v346TQkD++B3MJAxG4vI23/m1ituYSEnE+A/+lHHZMY4sUnNRFcn5JB5L5cvhndHU9zyz7Vbt2RzCU3XwmA3s1A7+mjWlSOVqtFVY6OpzUGI9G3XEh5yk7K5u1FGWZDlo+fprxZjrdT8WEqimrJ3FWCb6g7ITFHJgszGI1H9XAoTieXzvkNo05HpioTr4WuZhO/ZhegAnrAbDRyR7gfE6J742ZufIhDcTpZGhrNZT8t4J4+3enXJaK5r7JR3y15k5ujLmvSsbIkcW1kJBeFhvHU3hS+yK1gVt4GHu0WwRVhIS3+mRUE4eQlgpCTnCRJjPHzZMPQRB5NzeajrCIu2LKPH/vEtKhHZM+epygq/gedzott226gV68PjxmIDIrx51udhtP3pSM3adrj0ZwOJxa7tUXnHlMjfXqyXkPl97nI/RQkycX5VqQTdySWF9Yy6+l12K11u/EmjgxlzGXHTm//1doNrMnIZqfGQG83E1f2TiLUzUiwX9N2RD6WKksFP1b+hi1/G16zdlFbrUM+434MIy6CRn5G1NoqrCvm4Nj+N9hqkYNjMV357FE/U/Okbfw1+tVmtces1fB890Ru62Ljzu1beWBvAW8fyOSNxO4M8xW9n4JwKhFByH+EJEk8FRtOhFHPo6k53L4rg7cTo5pdjlOxoJGNaLV1n9adjupGj02K9OGt4iqmLdnBN8MTCPZuXoIqjVbDyBEj+e7dL7jgukvx8G95OnGVRq+nAMhjq1GqNB2yFX1hRiV2q5PTLo5j2fd72Lk856gg5PGFS1hWZcVDdXJX354k+fsR6N2y90NRnGzbOJfa5LkYrRWoGh1usWeQcOGr+PuGY9u5HtsP/0P54wnofzXGc+5B0mjrUtOv+hXnis/QlG7H6ZOItvtoJO9glPnPYts+BUPPEZRmpnLVjxfgIZk4TQrHzdSydoYb9cwZMJA1pRXcsWMH525NY7iHk9eSeoj5IoJwihBByH/M9eEBZFpsfJRVxDg/T84Oal4a7bjYh9mb+ixWSy7dE54nOPis4x5/Qd8I3igsoc/mFPYP74GpPjnV3rWr2LLgN0weXoy89Oqjls4e1HN0P7y8Pfnp8x+44p7rWhckNNIp4bDWYttRi/+0/i0vuxWievgR0s2LZd/vQW/SMu2uPkc8n1tayvtab/ZO6o6HsXUXX4fDzpbPL6U2sCe9zn0VL++j59XoEweif3whjrwMrN88iOV/CWjNWlRLFYq5O3KPszCc+SOSfGiuRs3fb+NY9hn6pGGs3fYX+30dbLx4EXpD6zOjDvHxZM3wIXydncOTqZkMWbOdK4PNPBIbi1nMFxGE/zQxMfU/qMjmoMfK7VwW4scrCUeP/x9MytWa9OuHs1mdfLPuAF+XlTN7ZHf0Sg0f3noN4QlJZO5MBuCeWb8dt4y5b35LryF96Taoe4vaUJ5bwv7vN9Hn7kMTU2sKcyj4ezlKnoI+yUTouKnIsmvj7qZOTFVVFUu1HYNJi6yRsSoKC1JSWZSRzapaJzcFe3HtkIGtakuNw8Ga72/HM2owA0Ze3eTzlNoq7AvfQz/+RiTTsX/nnAUZWD69HalsN7dEKTw1+jsiug9oVXuPpdrh5NnUvXyeW4VRcvJwTBhXhYeJ+SKCcBIQE1MFgIZkZstLK7lrVwYV9lqSi/diUxwUEoAim9E6inggQuK2+LGtHqLQGzRcMzIG8/p0rly+m1kjo5EkCYfddtSxSz7/g01pyfi5+9Rl8qyXVZ1PTEm3FrdBZzKgL1bY+uwCoO6ib6+2EjJJR+hF05Bl136iLipahoodSVvTpOMlScLkrmdJ8nZe3J+H3W4nXrEwoWsXnkyIw9Ps1rr2lBey/7triUiaSmwzAhAA2eSO4az7jnuMJjAS84O/sP+fF7l12zdtEoBA3XyRZxISuDnaxowd25mZWsQ7BzJ5o0d3hvt4t0mdgiB0HBGE/AfFmo3cHx3M+1mF/FlUTrnDgVH1ZvGQAXQ1G7E7HeRbLdyxbRWfLZ7DAzERXBQ9uNX1Dgrx5r38ElS9O2ff9whbFvxOjzHjGXbhodUTtZINrV7LVfff0PCYs9JGbXIRslmL6lSRNM0Pity8zSQ+fUbD/by8FBYs+IKBI59t3YuqpygOsrK/ITd3NlZrHjqdDxIyGq1EdtoKwrqOOOZ5e/LymLl6MyVOFa0s4abT8e7gnkQHuS4N/v7UNVT+9RAeE54hNrZtN9XLt2RhclRSXpWPl7trU/kfLtyo54f+/VhTWsGdO3Zx3pYDDHW380ZSLyLdxHwRQfivEMMxp4C3D2Tx9P4iwrQWNo488iK1o7yIu5LXU2mr5NnuSYwNSWpxPYqicP1f29HLMi+PisdsOnolyuOPP45GlXnkiUfrzrE5yX9lA84qOzhVZDctoY8ObXEbDqqpKeObb17g+uufO+LxteumYrXmoSh2YrrOICLiqobnnE4rZWUbOJD+LopiRa/3x2Yrprb2ABIyJlMk0V1ux8d7CBpN3TLfvIz1bNv4MGecc+Q+MmW1Fl5fsZpFNQ6e6hbK6KSWv6/Hs2/3UpQ/7sPn2l/x9267oKCBqrLnrxnErf0U5yNFaDRtv7uzoqp8m53P46np1CgaLgs28XhcgpgvIgidjBiOEY5pQ2khIOF/jEmESV7+LBwxiRWFWTywcyuPpqTwXGJfRgZ2aXY9sizz0cQevLdiHz9syuSa4V2POmZq33Hs3pvScF+ptOEst+E+PJSqlTkoNa7ZF6e0NAc385E/3qvXnIEs6Rg5Yj1lZetITr6F/QfeQVFqkWUDEhp0eh9Cgs/HYAzBYsnEoA8iKGgqsnzsi21w5EBW7Ajgml8/xmYJpFyro1aVqJU1THPX8dekUZj1LsxLcpjtaVuQFjxG0LXz2icAAZAk9J51uykXl6QSGNCyOTzNIUsSl4cHc05wAM+lpvFZbgVz8tfzv66hXBsZIeaLCMJJTPSEnAIKbXbGrtlMuaJhz8i+GDWNT0j9JXsvr6buoMShwV+nMNDTjYlBYZi0RnaV5bC/uozbE0YRaPRotIwFm7K4sryIvDF9jnru3adep2e3JEZeMh6om7tR/msaVWtykPRafC+Ox5Tg2+LXWlmVw+rVP5OyO4PRo0fQs+dUAFavGY8smxg8aF6Ly27M/oosft90P6f3eBtfrYy/uztF1dUEeZ146Wpe3jzSMz5CqzETG/s/PD17NanOXTsWYV/yAjGXfYH5GCtg2tKut3pSqzPiN/ohIuPOQnLRBOemyrTYuG/nTpaUQ5i2hjeTEhnu692ubRAE4WgibbvQqB9z87ltdy4XBWh5o0ePJp2zsfgAv+UeYE15DVV2K9FmDzaWZFOi7826QVFEmo9c/ltYUslXC3fwsaeOGy1OzgrzorKmCg93D2SNjCxJfPLzV4zsMoDTr5p6xLmqQwFZQpJb/ql269afWb58NX379iQhYQR+ftFAXRbYZcsHMmb0Lpevjjno03UPkxR6OoPDx5z44Hp2ewXLVwzE13c4xcV1K2xOH7vvhOdt2r6Ifj+eS/ltW/Hyj25pk1vMYiln78aPkDd8SmnEAGJPm0mgbxxZe/8gIv7MdmvH2rIq7tyxkwM2fd18kR69RH4RQehAYjhGaNT5IUE8tnc/Bdamf8v7+0XTv/5CfrhHt/3N1DUZXBDswSNJhy66acVlvOqn5cqcAuJVB3sq8kgtTCfKJxStVDd+3y84EU+fo3sIJG3rPk1brbUsX76a669/FIOhbu8VRVHYtft+8vJ+JizskjYLQAAmJN7Jos23MyhsdJNXG8myDo3GjZqa/U2uZ83q7+i74HYyb9tOhL/r0q83h9HoRc/h91LT42Kkv/+H+d3h7De507W6nHWDr2bAhFfa9L0+aLC3O6uGDWyYLyLyiwjCyadZPSHvvfce7733HgcOHAAgKSmJRx99lEmTJgGQl5fHfffdx8KFC6msrCQ+Pp6HHnqI8847r8kNEj0hbWNTeRWTN6XyfmIYZwcFtLo8u+Jk0JLfiXYzc3FUX/p6utHNzcB3ybv5NqeIn8cPQ6dpvwvB3LkvEhYWTP8e05BN7pTtWkyZbQv7S9+nX99v8fFp/eqfE/l443N09+vB8Oim9wZUVu4gK+trtFoPIiOvx2Bo/Huzdc13sPEz/C76hPAOCkAaU1NdSFlVDoYPx5DvHYZdcVDdZQThSRcQGXPGiQtopWqHk2f2pvJFXiVGycmj3SK5IixYzBcRhHbU5sMxv/76KxqNhtjYWFRV5YsvvuCll15i8+bNJCUlccYZZ1BWVsbbb7+Nv78/3377LY899hgbNmygb9++bfYihONTVZUxa9ZRYlfZPHIwGhf9YbY77dyy7EnGxN5EigVSq63IEqSUVhBTUcyLge4ExMVh9Gle1lZFUbDX1qIzmY5IqGa1lpOc/Bddu/bD1zeW/fvXsmbNfCorq7FYnUyO0FJQsxBFduBmD0GWTBSHJDN48B+4u8e55DUfT35tOauWXkBi/OPEdxnm0rJzU5ZQPP9xutz0F2a90aVlu1JO5ioc319K2aDpGPTuVOyYg39JBpakaSRMeqPN549kWmzcvX0bKyq1ROmtvJ6YyFCfxucvCYLgOh0yJ8TX15eXXnqJ6dOn4+7uznvvvccVV1zR8Lyfnx8vvPAC1113XZPKE0GI683JLeDW3Tl82SOS8f4+Lt0/5c7Fd/Lk8CfxMtQNsdidCuu/+4F1eQV0sVVTVl2Nw+msP7ruR00vywzu2ZPYiy+mYN8+dixeTEZ2Dk6rBeo3w9NKYFVVgnQ6QmNjcSoq29K2oe2yD6cjAbtdxdvbxODB4wkP70NNxlZ2rbuNpKHv49V1EAAl2+aza/eDDL9ws8te74kU/nwBPlsXsWXShwwYdL5LykzevpCg329DuWY+wYHRLimzPVltVaz87hzMOjcGX/pLu9S5qrScO7bvIMvhxhgviZcTuxNmbJtVSoIg1GnXOSFOp5PZs2dTXV3N0KF1eR2GDRvGrFmzmDJlCt7e3vzwww9YLBZGjx7d0mqEVqp0OJmZsp8gnZYn9+zhmu06NCi4yQ6iDBLXRHbh7KCA466YOULWBtj4Gbj5UTLgasqsZWjrx/9Vu52iZ58lLiSUoffc3miwU7JzJ/PefptlW7fhJcvEJnZnyPRrMUVEHHGOoihkbNtGzqZNaDQapl18HY7iZaRnfoS3W390dhvVa15nExnonO4kDnmjIQABcDhrsfhXkLn0TSJG3dHyN7EZLPYsisZNx77vH1anLaHvOS9iNLQsG6qqqqRn7cR9/oOY71iP+Rgp1fPKLbgbtbgbOu/0LoPendFX/En5c6Fk7V9MeJexbV7nMB8v1o0YyqcZmTyTlsPg1du4KdyXe7pGY2rqz7ogCG2u2T0hycnJDB06FIvFgru7O99++y2TJ08GoKysjIsuuogFCxag1Wpxc3Nj9uzZnHFG42PCVqsVq/XQdu4VFRVERESInhAXGbFqHanWQ58Azws0E23UkF1bzfryKvbZTLhLVt5I7MbkAN/j95LUlMBrSeAZCsWpqMDrZz7Op9s/5ZXBz9L9rfl4nTkVz4kT2/Q1WXIOULJ3EQ65CrNvLF5dRqJ1O7rL3V5bwbLVfYmX7yZ89G1t2qaDSv+8Gjl0IF69b2XThrlo17yHfuLTJHRr+pyUispiCj6aSLeKPaR6xpPV/yZGj7r2iGMUReXmbzYyf0c+AC+e14sLB3aueSL/lvxuP/zP/5KQwKatznKVSoeTh3bvYHaBHW+Ng+fju3FW0Al+1gVBaLaW9IQ0+yNBfHw8W7ZsYe3atdx8881cddVV7Ny5E4BHHnmEsrIy/v77bzZs2MCMGTO48MILSU5ObrS85557Di8vr4Z/ERGd+w/pyebsIB+6Gay4SQ6Mkp0bI0K5L6Yrr/foycrhQ1k+KIEog8z0HZm8uC/t+IU5LGCvhYC6beglINYnFoANc97H/4brWxWA/LX/L6bPn87M5TPJr85v9DhjaDSho6YTOfJO/JImHzMAAXBUFwEQOOjSJtWfnZ3NunXrKCgoaH7j60mSDGrd8FO/AecSeflXVC59lZXznsCpOE9wdp3tvz5Ct4o97Dz7G7rNWHdUAAKwt6CK+TvyuX1s3X4798/Z1uI2twebpZyEwrR2D0AAPLQa3uzRi+WDk4gwKNy4K5Nxazeyq6q23dsiCMKRWj0nZNy4ccTExHD//ffTrVs3tm/fTtJhKarHjRtHt27deP/99495vugJ6XiqqnJz8nZ+LnZymqeTB2Li6O/dyGS+TV/CmvfBzRcmvcjgv69hfNR4bvy+iuBHHkLXwpU3mZWZTJk7hUEhg1ibuxaA5KsaD16batHiGHTFBk67YOdxj0tJSeG7775DkiRUVeWcc86hd+/eza6v7K/rkIJ74NXnrobHVEVhw5L3Ye9CQs5/h3C/0OOWsWPXUnR/3IP+gs+Ijux5zGMsdicTX1/GgeK6DfQGd/Fl1o2tT3ffltZ8NQmdvRaf4TPoGn9Wh7Xjj/xC7t29l1LFyLkBJp6Kj8VX13mHswThZNEheUIURcFqtVJTU/fH8N/bw2s0moat44/FYDBgMIgEQx1JkiReS0oiMm0/X+UUcubmPTwbG0aNw87eqjJO8w9hWpBf3XLHflfW/as3KmIUngZPDGPOJv+13ZiH7sBn2uiG5x256dg2L8UQG48mtvEhCVmSkSQJu9Pu0tfmn98bp3r0br7/lpGRgcFgYMKECcybN4/Fixe3KAiRJBn1Xz0ekiwzcOwt5HQdROl3V5Ax4gGG9Tn2EOWm7Ytw+/sRPC77jpDg2EbrMeo0/Hr7CJbuKcTf3cDgLi3PMtsaFSnbsRfn4Nl7ODqz+bjHDrniT1KTv6Xi9xmsW/UaUvez6N3/JvS6o7cTaEuTgwIYF+DHK/tSeTerjN+LNvNgl1CuiwhF24pkeYIgNF+zekJmzpzJpEmTiIyMpLKykm+//ZYXXniB+fPnM3r0aBITEwkJCeHll1/Gz8+Pn3/+mfvuu4/ffvutYd7IiYjVMR2r2unkzHUb2WnRg6rgJdsoV4101Vu5PjKMq8LDjsq98O2ub9m6awM3V1+Mbk8WztwtSPl/AaBxN6EP9KR6dzaGyBD87n0SXXz/Y9a9KGMRc/bMIdAtkNv63oa/yb9Vr0VVVZZ9G0ffYd/h2eX4W8+XlJTw9ddfU1JSQmBgIFdffTVubs2fUFr+962o3hF4D3jwmM/ba8vZ/uNdlBv9GXr20xh0dQF4euY2in5/GJvBk74Xv4vxGJNQO5uM125Gq1Tg0HpjrNyN8aJ38OwWj62qCp27e6NzLmprSyjIXEnl8pcJyd2B38NF7dzyQwptdu5K3sqicg1hejuvJyYw0rfzv/eC0Bm1+RLd6dOns2jRInJzc/Hy8qJXr1488MADjB9ftw/I3r17efDBB1mxYgVVVVV069aNe++994glu23xIgTXO1BrRSdJhBn1zC8s5rGUPRywm/CULFwfEcC9XbsiSRL7yvZxw/wbGG88jcG74hjz4CUUv/gJHhP7oe91KDeMardR8cWrFLzzKVHffI0+cdBxancNRXHwz5J4pFqJONNdhI89/uRUVVWxWCwYjcYWT1osXzID1eiJ95DHj3vcltVfI23+Cr+pr5Cx5hOoKSHmjAcJCm37DeFaw1FZRtGqBdhTlqGr3E/gg78ia2Qq9++hZtY9GKzZqCq4q9kUxdxMyBWP1Z14jPdz/R+3I+dspv91K9r5VRxtY3kFtyQnk243M9ITXknsLlLAC0Izib1jhDa1prSC51P3sqZKQ3elivuLcjBlq+htWvxjwqCnO3EJPXCkplDx02p877v6qDKsm5aSfu1NRLzwEPoBY9GcYH5Ea1kzd7JmzbmEa88h5pzn2rQugPLlM1ElCe8Rz57w2JyC/VR9dwWRNZkY79kN+rYZlsj74zucGevxnHAj7lGxrUoYlv/U6dhDhmHudwY+fUccFVw4bU40eg1Ou42816/BvXYnDq03pis/wy08+ohjdyx/nqRFz2F/OA+dtomvXVWPGdC4gqqqfJmVyZOp2VjQc0OYN/fFdMFNLOkVhCYRQYjQLr7IyuGBvQXcpVYx1c+EWZLYt241XkEh9J14JlqdjpI3vsfc24xh9NEpzGsWzaHyx6+wpmcjaSQkgx7ZzYTGwx33SefiNuXyFvVEOPMzsKz6C8vmNVh278VZY0HWQd6lVgZc0D6rR8rXPIViKcZn9OtNO0FR4Le7YOcvMHomDLmp1W3ImfUmmqBumOIGUfL3l/js/YjaMS/B2g+RHdU4PLvgOeEm3BOOP0QF4LRZqNixmeoDKaj7V+FWswuvu+ahdW96FtzMb9/Abe83OLpfROD5dyHJden8d6x5E48lzxP5YM6JC6nMhx+uhKx10GUUXPgFGE+8S3FLVDucPLx7B7MK7HhqnLwQHyOW9ApCE4ggRGg3l67cTbHNwXfxPnx+z814B4VQmpuN3mTi9s9no1TXUvzqXHyvHYYmrEuj5aiKglpVilKYjbMgk/Kv3qV2fwHhX/yEJjC80fMc2fuwrPwLy5a1WPbsQ7HY0LgZMMbHYuw7GOOwiWiCo6G6iK0rz6T3Gavb4F2oa7/DUoAkyWhNgVRuehNb4Sb8JnzevIJK0+H7S8BWDRd8CaHNmxTrqK6keM3fULKfoB1PkB18HfrKPTjMEQTf8AaSVtdwbNGmddQufhedsww5biwEdMeZvhE1Yw0O9ygMvSbhRI81ZQXueQux+fVD5xeMuf9ETF16ImubP599b3IGxm8vozymDz2ue4vkxY9g2vQ10umPEdP36hMX8M9zsOotGPsQzP8feEfCXa1fPXU8adUWbk7ezNZaEz1NDt7qmUSCuX0n0QrCyUTsoiu0m1H+njyTmU9leRmqohCR2JPS3GxstXW5F2SzCe8LB1D6+TL87gtH0uuOKkNVVb7fkMWqfcX0i/TmqqGTCRg8id2JSVTP+xyPq+5H0umx71yFZfNaLFs3YE3dj2Kzo/Vww5gQj9uw0fjc8wIav5DjtNZ1cXbVxjdQd/yIw1KET04ONW469DYHNT7+OPyj0JZkI7dkuadPFNy8CpJ/hK/PgcihcN4noDv2PjGKolKdmU7Fzo04960koHAu+I/FGdSX6uuSCQuPbLQq/36DoN8gKjMyKN3wD84tC8E9CM9ek5GLc7Bv/gnZaMIjYQxeF9+BztO7+a+nntPh5Osvd5Cxp5SJl/1I0K+TWPvWCOyUk3DXThbs28bHCz7B1+hD98BwhkYm4Od2jD9eQYlgr4ZlL9XdHzC9xW1qqq5mI/OHDOX3/ALu253KmHW7uDDQxJNxsXiJJb2C4BKiJ0RokbVlVUzbnMoDln24HdhJaUEuepOJyO49GR84Dq1GC5KEZW0yWmcaXrdeB/+ai/BHci63fLOJfpHebMooY2SsP19NH4xtxwbKP3uZmq0pSAYtWiUf4/ApmAaMwDB4ArJ301fNqFVFbFs5ld4T1rT4tdqLd2P5fiqKtQKvCitV8cNQep6DrPcC3664+w9EVRRq9/+K01aGMXAwOr+EFteH0wHzboc9f8K4J6D/VUcdUvNIBJXm3mAOQjL7out9Fj79R7a8zjaQvKOQ377ciU93b6Zf3gOdVoPFauOJ7x7j15pteLl5opNMTImZRHFtMZnlBaRWbOPeQbdzQY9RRxeYuqhu24AuIyHKtRsEnohVUXgpNZX3s8vRSyoPx4RzdXiI2KVXEA4jhmOEduNQVLosWccIKYcLDTYkte6P8arSNYzyHsFY79NQVRUUkHP/wehYDVNePWJS4XfrMnj45+1cNDCCb9dmEOplZNXM013aTrWqgG0rp9F7QvOHY8q3voPXT/87VNYjBUiadlwxUZRaN0QDcPF3KD5dyPjiRdyjY7HV2FC2zSHsgR+QNJr2a1MTWK0OPv1oK8W51Vx4XS/iungf87isshKC3D3RHTa8k1VexrXzHiLQFMTX5z/aTi1uujyrnTu3b2VphZYonZV3eiYxwMu9o5slCJ2CCEKEdjVkxSrCDFrmDDy03La4tphr5l/DvLPnHXnwhk+hYBdMerEhELE5FJ79Yxer9hXRL9KHR89MxE3v2m5upTKP7avOoVczghDFYaV8wfX4rPuF4kFT8Rn/IbLu+Im4lqQUsHBnPgkhnlw6KBKNK5NebfwC/n4MW/hp6Pf+QpbHOXhWb0Jy1FLZbyahZx+d1r2jrN6Qy+IfUogaEMil5ycclbywqU778hLem/gCSYGNDyt1pFWl5dy2fSc5DhNneEu8nJhIoOHoIUdBOJWIOSFCu6pRZMz/+hRu0prQSBp2FO8gye9Q+n4GXAvrPoL5D8GEZ0CS0GtlHj8ribakSgoSTQ8Iqvf9jPOv+5ASJ6M+WopfEy6imzNKufqz9XQNMPPN2gy+W5vBH3e6cGik/1XYogag+XQyChKmcTci+4RQuelvgide5Lp6WqGqxs4H72/GXm7jqrv7Ex7SSNr/Jpox4A6u/f1OPpj4En1Curmola4zzMeL9SOG8GFGBi+k5dF/1RbuivLnjuhodCLrqiA0mVgAL7TI/Tt3UuA0Eu52ZFe0m86Nd/rewxW/XcKCn64kPeuwuRiDrgevcFj4SF2+h3agqkrDUNHxOC0llM6eiH3pkxgumYf3mDeanE+joLJu76Mx8YEA7MytaHmDD2PNW0/pn1dR/l4CNb9cRuXQS9k75VW2HJiFOSKKkLOvQzYev4emPcxfkcEbj62kW6wPDz4xotUBCMDZiUPxctPy6dbZLmhh29BIEjdHRbF5RH8m+xl46UAp/VasZVFRaUc3TRBOGmI4RmiRoStWk+UwkDy8F96HrxRQnDhf68Fig4ZSayk/u5sJjp9KtGc0Z3c7m0jPSFj9DlTmwvin2izx1EGOigxSVl1K0sTGs3JWbH0HafkrqINvxHPgA82vw6nw1G87WbAzn4RgD144vxeBHsde1XI8qqJQm/4X1q0fo8lJxunuh5x4Lu49b0Bj8G44bvmCe3F3G0zfERc0uw6Amo0bKZs7F11QEL7XXovGvWVzGorKLXzw3mZMdpWrbumLn5/rlq/W2p1cNmsFe22f0K9rT67rfhZ5liqmhfdB38nmwBy0vbKGW5KT2WM1MNBs5+2evYgSWVeFU4iYEyK0m7Gr11LjdLJmxL9WKThs8EIUhA+A/csAqH0ol7Ty/dz49424ad14fuTz9EtbDRU5cMbTbRqIOCozSV5xJn3O2ICkOXL00V5xgOqfLkbVGfGY9h1a8/GW+bYN1WmnOuU77MlfoylKw+EXibbHpbgnXIGsPfYFzG638M+fl9C776sERTSeg+WY5+bns2/ceHQREdjS0gDovntXi9r+8otriI33Zdq0uBad35hf9uTz6O87mdA3lGdHduP9vYuZtft7fEwBZFcc4OzY83ikT8sCsLamqio/5Obz8J50qlUtVwe78XBcvMi6KpwSRBAitJsz1qwh1waLBvdHL0tH9obs/gOWPg+yDsY/AdEjgLo/0GnlaUyfP53re11PQOYmejlUgie90qaBSPbKWyksX02ZtowBiR9iDh1L+epHkDd/j3z6E7h3v7zN6j4WxVZJ9fZPcO6YjVxZgCM4AX3vazB3ObvJQ0BFebvYuOZxxp/1XbMmf1rT0kibPAXPyZOp+OMPoOVByEtPruTC63sR5YLhF4Ayi52rf9pKQaWFT87vQ3ffo3torA4HU+c/gAYHn4x+hDBz6zY5bCvVTifP7E3l89wq3CU7z8ZFc15IkMi6KvyniSBEaDfryso5a/N+ALQ4+K53DCN9m5bK267YmbNnDlpZy/xtn/Kh3Rv7aTPRh/Vs9BxVVdm3fiElxUVIgFSfgEwCJOmw2w2Pq/XTUdWG29bSHRSWrKW/koY2YhBeU75E1rXP8kpndT7VW95BTfkNrNU4Iwdg7HMTbmEtn8C6auEL2K02Rk19pFnnlXz5FaWzZqELCiTo4UcwdG1ebwrAc4+uwF5m5bZnRuDr0fohh8+Ts3ll4R4uGxbFg0O6nvD4t3Yt4LNtb3NTn9tZk7eVYPdwnu57YYtX47SVA7VWbk/ezvpqDQmGWj7o1Zt49+bvziwIJwMRhAjtakNZJRvKy/kwIxsvrcI/Q4c2+dwlyUvIzMlk8fbFxFi6EmGowU2joCDhdNjQy6CVIbncTH+fSoosGqL8jIQG+KFKh8INFQkkCVXlyLBDkkDl0LH1t3tvfIDs3tcRds4rbfGWHMFespeazW/BvsWoqCgxp+HW9zaMvq5ZEaSqKkv/fBBvnx70Gdr0napbY9GSdNauyEaqdjDzudNaXV5ulYUr5mxBVVS+OL8v4c2YS/Poxi/Jq61geHA/Fmeu4kDqH/gZvV3S2yAhoaI2rKz699cjjqv/E6qgNNxW6/87qNgYQ6r3eBStP5f7uvNYj254aDvn3BZBaCmxRFdoVwO8PRjg7UFmTSVf51VS7XQetWT3WJ758hmsNVa6xnVl8uDJnDv0XDQaDZXZKRhMHuh9QrDU1mCrLGZowT5qa61EDjjDJZ9yC6K6of+z+ZNPm8pasY/qDa+h2bMIRWeAhEm4X/YXOnPj++C0lCRJRMeNIHX/Q4DrgxBVVbE6FCx2Z90/i5ONa3IYOCyM4YNaP3/m5fUH+GL5fu4Y243r+0Q0+/wn+1/ZcPv8yIE8vlnH1BETGdI1utVtO0hV64IJRVE4PP5QUUGqe16WZSQkZFlGlv51+7CAyOJUeHNfFm9lFfHr0i082i2USyODxRCNcEoTQYjQatMju/BZ3g4e3LmLt3r2OOHx9jQ7Tz7+5FGPe4TFN9w2upkxupnxDHJtsipdQDewlZGz4Q8CY/uh9QpudZmKo5bitY+i2f0nWqeMNmEK5iuXonELdEGLjy8wdDh7dgeTl55GcNSJhzGaasfOIn75aieSBBpZQpYkZI2EQSvTu1cAbu76FpedWlbDVT9uxteoY+nNw/E1tbysg978Yz6e3WIZGR/b6YZkDjJqZO6Pi+TSyGAeTE7lnrR8PjmQy+t94+nl2fFLrQWhI4ggRGi1rmYj14V68GF2NecUlzLWr/G5IWl5aVj9re3YuiP5hESTPex+nFtnY/n9etweyWz1Rat64xsELPwQ21U/ou8y3kUtbRo3N18GDH6b9avuRm98E9+gqFaVZ7U6+PyjbRTkVnH+9T3p3rVp83yaQlVVHl2Vyi9rs3h0UgLnd3fdaiR7bS0e3j6dNgA5XLhRz9cDE1lWXMG92/cxYcMezvUy8lSvWHzFxnjCKabz/8YKJ4XH42Lx09p4ek/KcY/bX7QfvU5f173dBFXrcsl/ZwvFX+/EUeaa4CXs9BvxGHMHZeZuLrloeQx+EICajAWtLqslfAPi6NP/BdavuZOq8uIWl7N2bQ6vPLISY6CRh54a6dIAxKEoDHhnObuzKlh160iXBiAAgcHBFGzb7NIy29ppfp6sHNmH/0UF8kd5LQOXbeWDtGycnWuaniC0KRGECC4hSxIhOgXdCYa3T+9xOlKuxAvfvHDCMu0FNZTNTUXjrqN2ezF5z69zUWvBO7o3+tp8LGV5rSpHVRyUzp9Ohb8fusjRzT7fbrezd+9esrOzW9WOoPAE4uMfYPWym7HZapt1bmWFlTdfXMuCv9K4YkZ/rrowEdnFqcdf3ZBOcVYl353fF3eD6z/tdw8NQfHx5YEPP+W+t97li5VrXV5HW9DJErfHhLFuRE9O9zbzWHohI5ZuZk2Ja7LuCkJnJ/r+BJfJsEpM9T/xkteBkway5q+NzNq2kot6DW/0OEkjgQTOSpsrm0nW8m/QrHwV1S0QSW75pmM1mYuw/3YzxIzG45Y9SHLzfp2cTicff/wx+fn5AMTFxXHppZe2uD3RCUOprihlyZ83c8a0z5t0zt8L9rNuYTpxp4dz+4SYNpskuSG9lGumxqNto6Rdo+K7MSq+GzuKSwl1N/PsV98y22jggv592qQ+VwvQ6/iwfwI3llVx17a9nL01jcnuel7oHUuAXmyMJ/x3iZ4QwSWyLDYqVCODfE88GXPK4CkkXjqJO4vNfLJ5O42tEtf6mfC7KgmtjxH3EWGEPjrEJW3VrnwVzdlvE3rPMgyefs0+X7FVUfrzudj/vBPDud/ic8aHzQ5AAEpLS8nPz2fEiLpkbnv27Gl2Gf+WNGgyKiVk799+3OOKCqt59clVrN2Uxw0PDeH8id3adJVGkKeRCqu9zco/KMnPBx+DnkevuJQ1S5eyOSOzzet0pf7e7iwZ2YdnugSxtNLCwBXJvJ2aiUMRQzTCf5PoCRFcYl1ZJQAjfL2bdLzJaGKoLp31pb78MX857w3pRaD30eeaEnwxJfi6sKXgiB6FY+lbWEPjmh2EVO/8GmXxY0h9L8bzrB+bnOH0WHx9fenWrRsrVtTtazNmzJgWl3W4/oNfY8u659m5zYaf32ii48bhG1i3BFZVVH6Zu4ft63IZeHYME4Y1f2lsS9RYHWg07feJPr+yCkmSCPN17c9Oe9BIEtOjQzgnLIBHtu/jmYwivsoq5NWe3RjuJ3InCf8tIlmZ4BKFNju9V27j7khf7os5OgPn7vJ8pm7cgQd1wUqNqmOqn45X+oznq+0pfJJZiK2ynG/HDSO6iZlXW0xRODD/XTS7fyHi7oVNOsVZlUfVLxfjVCy4T/sOvWfzs4wei6qqFBcXYzQacW/hRnKNKS/JIW3XQoqKF2LUxRMWextzP9+OI9DIrdf3wcPUfkFBwgt/896l/RgT0T5BQUlVNW++/BKPP/54u9TXljaXV3PX1r2kOGGCWceLveMIMoghGqHzERlThQ510cZNLC2HvmYHX/Xtx6f7VvNRVjHuGhknMpeH+HJ/92GNnr9kfwY378pg5ej++Lq5bkfWxuQ/0wvPWxaQP/teJHsNbiNuprYwDWnXrwRd+zV6szeoKtXrXsa5/j2co2bg0/O2Nm9XSymKA0WxU21zUF5dQZXFSkVNLgUlezFWv0jW3rPoPvJuhvYIavO2WOxOdJq6rLZTvlmP1a6w4MrB6LXtNwJ8/xffMrpXDyb37dVudbYVRVX5MiOPp/fl4EDi7ogAbokJR+fiCcSC0BoiCBE6VIXDycv7Uvkop5ogncLtYSZ+zdnLz8PPbnIZLy9ezg8WGOOu586e8YT6eLdZezM+uhyvnGWU9r4Rj9hhVK34CIcqoY8aiLzte5weofgY0rF4++M95Rt0xs61WVpOcTbLtn6CtXYPEhZAwqnq0GlUkNzRavTIGj/M7l2JCkwkMbI/suzaVOErskrJrLBgcyrsLanmjgFRvL4hnR+XH0Bv0oIkkRTuxawL+7m03qZ4/vf5VFdUcuaQgVhUid6hQXgZW7/PzUGKxUHtjmJkgwZjd7+6idRtrNTu4LHt+5hdWkOkrPJ6r24M9RV/J4XOQQQhQqfwU24eN++uW/rq5sjmzaQ4pobGNvl8u8PBZzv28ENeKVrFyTAfTy6OjSLW1zX7ghykKgpl+Rl4B0Uec27H6tfORtM9jkETX3RZnS1lsVnZuG8NO9PmYdKpqI50nKon4aFnMbT7eNwM5g5J/93tmQXoTVqMWhlVltDLMmG+JmaMiKG7n5kqm4Mor47ZsM2pKLy/dCUFRcXoVIWKrAwiE7oTFx3NuITWZVZVFZX8NzbhyK8BQDJqCHu88V4+V9tSXsUdW/eyxykx1UPP873i8NeLKX5CxxJBiNBpBP+zpe6GqqKT4c/+8fTwaP7FqKCyih93pfJkNXhbazGrCveEeHNxr+5tmh0zJ2cXn3/+Gddffx0BAXFtVs/xVFQX8dfal7HU7gVk9IZIErpeiF5rJDwgHk9T0zd7O2h7ZQ2VToWBnma0LujKv/XXZH5fmcGGR8eTU23huWX7eGdKD3yNnW/OQl6thbmr1lBQUkZ1QR5dk3pw46jhyC0I3pxVNnKfXot5aAjVq3MBCH++5Tsit4SiqnyWnsszaXmAyswuIVwbHYJG7EUjdBARhAidhk1RsCoqJXYH525OpcTu4I9+sXRvQSByuIzSMh7dtIssi5WrI4I4J74rZoPrutgPKizcxw8/fIQs67n55qP3uWlLqqry96bvyM39nqDQWxjTaxx6bes/5b6Qlstr6XU5SfSSRMrInphamLfj5vk72J5dTk5+NS+c25Pz41u/B097yq+u4atFS8jJysLdbObyiWcQFxTQrDLKft1H1aocAHzOicU8qGPegyKbg/8lpzKvwkKcrPBmnzj6eLl2krMgNIUIQoROqdBmZ/z6PRTa7HzWswtn+Hs16TxFsVJTcwCTKQKN5sjgJaW4lNeS9/CXQ8NrYd6c072bS9u8Z89yfvrpT5J6BDN1yh0uLft4ciqzmPXPfTidUdwy+THcDK6boDt49U4ijHpK7A52VluY26cbw3yad7E67/uN7MupwOpU+PHqQUgKJAZ5uKyN7a20tpY3/lhIbWUlL1x9WbPPVywOJI2EpHPtXJuWWFNayZ1b95KhSJzvbeTpnrF4ib1ohHbUkuu3SFYmtLkAvY5VQ7oTbzZyZfJ+Ltqyj7Sa4+8DY7HmsXrNeNaum8ySpT0pLV1zxPPxfj68P3ow6wcncHNeFV9s3enSNufn76W2Vt+uAcj3ye+yYMs9rLSez9vJY3h9U45Ly78i1I8VZVXsrLYQYzIwoBlzNWYuSWH0x6vYlVGGyU3HI1MTSQrwOKkDEAAfk4mHzp6CPmMfeeXNT5UuG7WdIgABGOLjwYrT+vK/qADmldUyYNlWvj6Q22gyQEHoDESYLLQLN43MooHxvJdZyEv7cxm2dhcT/Dy5MyqIfl6HtjFXVZUqp0JZwd8kW8ws9fgCuXIFpZtv5fyxG48q189cdyGdWWShYNUG7h3av9EJmut3H+CPf1ZRXZBBULde3HLBeEyNpMTWamUCA2tRVbVdJnwqipOAwldIGLCEq0dGkFVpYcTzixkU4sW4aNesyrktKoizAr2pdiokmI1Nel3ZFbVc8N1GQrxNvHNOT8LdjXj9x3JU6DQaHDEJfPT7Xzxy6YUd3ZxWqduLJpzzwwO5f9s+7t2fz+fpubzdL4GEVg6FCkJbEMMxQrurdSq8tD+XL3OKqXIq+Og0dDEZcJNltlfVUuZwopXAoYJOtWKXDMio3Bsdwowuxx53z6mo4pY1W9mryHzVL45unu54HrYcM7uwjHfe/5BJZ07D28ONddtSSElJqV8V8+9fAQlJcWC0FFPh7c2rd93VJu+D02nnt5TPKKvaD4B39QImnbYeff08jQ+3ZPLCb7vY+79xHbZF/YQv1+LrbuC7c/t0SP3txeJw8MxzzzH1yqsZHNU+WWTbw+Kicu7dkUaeEy73MfFYr1jMms7RcyP894g5IcJJxamq/FlYxs8FZaRUW7CrKiF6Hd3dTaiohOscnGfeh2qK58kMJz8VlHF3VBAPdD1yG/jiahv/pBXTK8Cd5Ipi7ksvIMhhI0B1ckd8FJl7C9i9YTlTp05lZJ/4Jrdvwksfc9NpEZzWvTd+nq6ddLircDPrdz+L1mciA0Lq9o4x6gxEekYfcdytf+1g+a4Ctt41ut2X4L65MZ3Xf9/NR1f253QX9cZ0ZmlFxbz7wxyeu3E6uv/QhdqqKLyems3bWYV4qArPxEdwdlhghyzpFv7bRBAi/Gepqsr9KVl8lVvM290jOTfAm205FTy0+QBIsEuroANCamG3j4xHtZNKs4aEnHS6phzgubuuIqiJ+9ocNHftIhbuyGBDlpF5t48mxCfkxCedgMVewy/bnqPCUsDpPR+jq2focY9fllXClW+vZtuTE/BsxzwQ5/y5jYy9pXxwYR8GBDdtIvF/wat/LEBC5e7JEzq6KS6XXmtlxrZUVtbYGaBVeatfAl3MbZ+ZWDh1tOT6LeaECCcFSZJ4MT6ctFord+7O4JONmfgoEjPjwxgRf+TSyvHPLuaTqwfg5+9Gcqo3kZPHE+Rz9Hj47upavs0pwUur4bpw/6NWEpw7+HTOHQzrU7dy3ae/8+7lo4kKavkqnHWZf7N7/7t4hVzDhf2mNumTaKi5LhdIdqUFT7+2W3a5raCSME8DK7LK8DZo2bw5j48u6XdKBSAAXYODWL15S0c3o01EmQz8OCiRPwrLeGDnAUau3cX1AR48mBSDoYOG+wRB9IQIJ5Vqp5NBS7ZT6VTYMbYXHtqju83XJ+fz4A/bmBAXyN3/b++u46O41gaO/2bW4u5CBBJIgOASChRvKXW3W+re3npLvb1v5Rbq7m7QltptSwV3DW4hAUKIEbfVOe8fCRIIkrDJ7ibn+2mazegzQ7Lz7NGLe2EwHblNjd3BgCWb8NWp7G2cYr5wdN+jnndt7iYe/WEh0y4ZTGpsnxYVZVeZ9/Hr2iepxpdzMx4m0rtlD/aH5mzlm9k5jB8Uy5uTeh1oM9IaVk3ji80FLMgpJSbAiz83FGK2OvD1MVBnsWPSq1jMDj69oj/9IjtXAlJcVc1n8xZRsq+E/17zL1eH06ZqHQ7+uy2PDwrKCRcOpvZMYkJUy2aUlqTDyeoYqVPYXW9h+OLNXBsewpMZXZrdxmy28ebMzfyzuZgnz+3F4P5N23QUWWz0WbyRsyOC+Lm4Ajh2EgIwe8Mqvl2+iZJqM0MTdFwweAjJ0elHTUiEECzK+Zbs/O+ISbyHCV1aP6x3UY2FB//ewtItJZw5MJZnR3c/ajJSVmdh4Z4KFuSWsnpPJSVVZgQN/fFtDo2YEB+GdQtDCMHIxBDGJ3T89h4n4o+t2cz+cSYP3X4bIb6doyfJ1lozd63bzhqzg1ON8PKAdGK8jK4OS/JQMgmROgW7Jug9Zx1nBPnz4oDkY26bnV/FfZ+soleAL49e1w8vn4PdSz/O38fbu4sJ0ut4PjWuSVfhYzHbbPy5dgU/Z2VTXmthWJKBCwYPJTGqx4FtSqt38tf6JygzpHFZxr8JdtKgY/P3lHPVG4sBCAj35rSMaIYnheJv0vHc7O0UltYhBMRG+NI/KYRzUiIYFO6PTlURQuDQBPoTKEmpqdlGYeFMjMYwYmMvR6frHG0HHnrpVW6b/C/iQ0NcHUq7EUIwo6CUx7bmYdY07ogJ5t/dk+QMvVKLySRE6hQWlFVx0docfu+fQr8TSBw0TfDWz5uZuSofTRO8cFkfBjlpOnuz1cZvWcv4NSuHKrOFEV296d6lkj0Vi+nW7WFGRfd0ynkOtbfaTL3dwcvLd/HrnFwMfgbiY/yxWB28eX4G2eX1XNi99ddns1WwaPGp6HTeWK0lAIwds8NZ4bdKhc1OrUMDGkp0VEBVFVSUxtegA1RUVAVUpfFnRWlSUpVvtrK2qg5VAQUFXeO2KgqqAh//8hvpsVHcP25U+1+ki1Xa7Dy9ZRdflVQRh4NX+6QwLLRzVclJJ0cmIVKn8H5uEU/mFpA3uk+LJx/7eV4ud/6+ieUPjSEiyLmf7ussVr5fPpuXl9Zy2an9uX9wklOP317q6nJZsnQcMdEXs7dgOuDaJGRuaRWTN+QS2TiwnKDh07vY/xrgkJ9psrzpsWodGrFeBgL0usbjgECgNb62OBzYy0oZF+jN06cMbIercz9rq+v499rtbLEJzvDR80K/HnKGXumEyN4xUqewpqSaWKG2avbTJ3/fwrAgP3yNzh8Hwsdk5F8jTue0ARYmf5/FL+sK+PzifiQEeFZVho9PEsnJ97Bnz2f4+fUgrcdzLo0nt97KsCA/vu7Ttc3PVWOxMHVZFfOqzW1+LnfVx9+Hf07J4NO8Yp7JzmfwgrU8lBjJ9cmxrfqbk6RjkSUhkkepsNnpP38Dl4QG8VzfxBbvv2RTMZd/toJUg5EeoX5Mu20wep2K0gb1319t3Mtzs7Zy8eB4Hhvu3An2PFGFzc4/ZVUgGqpB9AoYFAWdoqJXQYeCQVXQKQp6GoYg1ysKH+fvo9Lu4J2eiW0eY0ltHYOWbCLNUsNPpw13yuzFnqzEauORDTn8XFlPiqLxet9U+gbJGXql5snqGKlDKbHauGl5NtHeRh7v1YVwo55rlmczv6aWJcN7EnXYHCb2khL23HU39WvX4jssk9iXXkLn1/wbpt2u8fZPm5m1vhC7JjAaVGZOGYVO79zxEqqtNq7+cR35pXV8eFE/eoZ13jfwp7Pz+aawjO4+Xmg0jJgrGr9rAjRAE6LJdyHAKgQPJkVxYVT7NBbVhCDpn1W8GBfMhT3avvTFEywpr+auddnsdsBFQd78p3c3OUOvdASZhEgdhk0TpM1bR01jpb5Bgzidjp2agzdS47kg7sgxDUpee42yTz4l7LbbKJ46FWNiIl3/+P2EzvfCp1ms2FPBNw+OdHoiAvC/nBIe/nkjY3tGMm1sd5fNBeNKD27NQ6/AM6nuPzfL9rIKzluxhfsi/LgiI61DDePeWjZN8HZOPi/uLsaE4KmUOC6Nk8O/Swe15vnd+d4JJY9w7fLt1CB4oWss60/pyaUhgQQIla96JTWbgACYUlLQ6uoo/eADAIIuuvCEz3fXZb3YUF3nlNibMyk5nBV3jKSi3sbANxewKL+8zc7lSuU2OwvKqllYXs2i8mqWlFeztKKG5RU17K634qV6xsM8JSSIf4b14qEKO59mbXR1OG7BoCrc2S2OJcN6McjPi7uzCzh94Vq21tS7OjTJg8nyNMktWewN3TEf2JFPso+Jqf2O39MkYOJEVF9f6rPW4ps5FJ9Bg074fAtXFVCPoLzaQlhw2zQkNepUPjo7gyV7y7nt+3X0SQjm3ZMcAdWdTMst4P09+4hvHOxKNP6/oQcK2IXgli4RLoywZcIbBywz6fVomtYpS6+aE+Nl5MvBPfm7pJz7NuQyevkWrokI4OG0RDlDr9RisjpGckt2TTB44Ub2Ouyc7evHe4PbtmGnpml8/st23l22k4X/N77NHziapnHf7K38s6GI587uyRnJ4cffyU1tr6nnyvW5BBt0fNE7mbDD2up4sqzCEl7dsI1Sm50fTxshE5HDmB0a07bt4p2CcoIUwXNpiZwZGSKraDopWR0jdRh6VaHAYQfgvPi2n9NCVVUio33Zq9m58pn5nPXkP7w5ve2K4VVV5aVxaXx15UCenrWFC75dRVm9tc3O1xaEpnF31kYmrtzMnbGB/DGwe4dKQAD6RoXzzqih5FnsZJdXujoct+OlU3k0LYkFQ9PpajRww+Y8Lli6gV31FleHJnkImYRIbuvf0Q1zmgSZ2qfWcEy/aP53Yyav3pXJ9Cmn8tbqXbz7/SbasrCwZ5gfS28ZQb+4IMZ8uKTNzuNsefmbWf7BRSwv3svIuq3kbv7H1SG1GbvmoCAwlI37ylwdittK8vHix2G9ebd7LFvqrQxfspHnt+7GommuDk1yczIJkdzWXSkxKALuX7+rXc5nNOjomRxCuL8Jb5OeRQ+PZcOuCq59eRGVtW37ye7hYcno3Kpi9Eh2u506cy2LfphC8W+PEHvmMyyaMJ6HMvojdB130jNfo5GFveJ5Pjuf+bm7sWsaNVbPKrVqD4qicE5MOCtG9uWKMH9eyy8lc34W80urXB2a5MZkw1TJbXnpVG6OCuXtolJ21VtI8Da16/mDAky8dncm03/ZzoX/nc9dE7szKbP5WXs9lhCgORCaDaE5EMKB5nAgNDtodtYv+wrv3Lko5kpiqnOp1PvhM+Zp+p33LDTW+2tC0NFbAHQLD+WuhAju37Ibv807qULh3oRILu2Z6urQ3I6vXsfzGSlMrq7j9jVbuXhdDmf4m/hvRjfCjR2ruk46ebJhquTWttaaOXX5Fk7z9eXTwSkui6O4pJaHPliFzkvH1BsGEuTn3IRIE4JBb8xn1R2nOvW4R2Ox1lM7tQc7w/qgKTocig6hqAhFh6aoaKq+4XtQIj1G3U64fwg6BTTNgU7X9LNLSUURNy5bhr9moUTT8fuk89vlGlxlfWEx4zfv5b24QM5O8cz5gdqLJgSf7izg/3IKQFF4tGsMk7tEyuHfOyg5d4zU4XT39QJgVm2tS+OICPfloykj+WHWDi7673xuH5fCOacmujSmk2G3W9kRncmga79p0X6HJyAA4UGRzDztbACu+n2GU+JzZ2nhofxr204e31FAF18f+sY4Z0bmjkhVFK5JiuHs2AgeWLuVKTmFfLG7kNf7ppLu7+Pq8CQ3INuESG4vyo3GHjj/tK58e+9wfl6ex7VTF1Bbd3JtA4QQVNvs/JJdjK0dCyUdQkCHr0RpG3qdjqkjB/NwfBhnbN7DhoIiV4fk9kKNej4c1JMZvZOosDsYt2IrD2/YQa3D4erQJBeTSYjk9k7zb5hv5bs9+9q0p8qJCgny5sP7RxDsbWTOir0AlFc1NFx967uNnPv0bGb8mc3OnU27dDrsGt0f/B/PvL+a2jobH2/Ip/fLcxn9ziKe/XMrT5+R3m7X0NCOw3X30maror4+3y3+PVtra1k5AZZ6UsPbvgt5RzEiLJDFp/bj9uggPiuuZOj8tcwq7pijB0snRlbHSG7vsuQItq23cPv2PbyXU8z0zFSC3GDyLF+TntfmZLNs6z6+ySkiyWhih9XCH7cM542fN/Ptij3U2B3MuGc4/v4mdHqVZyakcd9fm/n72X3k2m3cdV46dw1p/3YFDpTjfgKptzq4d0YWc7eW0Ds2kNcv60dEgNeR25nr+GvzEuwCypUj1x+usPBnNm1+ECGs+Pv3ZuCA6aiq5/Wu+aPWzoe9kjr9TLstZVJVHk5L4tIu0dyZtZXJG3cxKjefl/umEm3yvN8D6eTIkhDJ7fUN9OWHU7rzYVoC6xxWeizcwFu7iiix2lwa17VnpOCrKZTWWPnnjhF88+9T2PZ/E0lJCOTVO4by3SOjOHdALKc+O5v/fpqFpmlcODaZe/smMDA2iHNqjTiKzS6JXYPjloT8um4vv60v5NZRXVmWW8bgZ5sfCyS7YBufFFSiWmu5Pz74uOfOy/sYf78ehIWNo7p6PYWFP7biClzv2rhwfshun+7jHVGyrxe/DMvgtZQYVtdayFy0gbd27GmsKpQ6C5mESB5BURQmRQXzfHIsAULh6ZwC+i7cyMULt/B7cQV2rf3fuBJjA5j59FjeumcYXWIDCAn1PmIG3pvP7MFvd49k/a5yxj36F3uKarj1wnSW7q3g9oeHYt5axdRP12J3tO+gThrKcZOQpDBfAD5b0vCgndQ7+qjbDvWyc+7QsxnZc/hxzx0aNoaq6nXs2/f3gZ890b/SuvKHTcVmt7s6FI+lKAoXx0WwYkQfJgX58PTufYyen8W6Ktc2RJfaj+yiK3kcIQTFVjs/7inl810lZCsOktDx9dBUEtt5LJGW+OafHN6btwODBqF+Jr56aCRWm4PX7p3PwOvTGJUR1W6xFFaXUTTjZvpcO/2Y2y3ZUcr87Q3VMRN7RTU7J8j63LX8lruVB8dcfELnFkJQWbkKq62UkODh6PW+rboGd/DqirV8v6+K1/un0ScyzNXheLxl5dXcsXY7ezSFf4X58XjPZDkpngdpzfNbJiGSx1tVWcO1K3dQpArO9Pfjrf7JGD1gorFt+VX877mVGDMjuO2KXu123l3ZS6ia9QSWQTcycPCFJ328DTvX8b+cLSechHQ0GwuLuXrNNlZMPH4pkHR8Vk3jle15vJ5fSiCCF3smcVpkiKvDkk6AnMBO6pQGBPqx+NRe9PXy4tfqGtLmrafK7v5d/8IDTWiKwpXndW+X89XXVbDim9vJW/gO4Zd96pQEBGDd3hzMLqgOcxd7qms9Iun1FEZV5YHuCSzI7EmiSc/kTbu5fNlGiiyubQMmtY0W/eW8/fbbZGRkEBAQQEBAAJmZmfz+++9NtlmyZAljxozB19eXgIAARo4cSX19vVODlqTD+ep1/JHZg9sjQ6lFcNHirW7f/TPYz4TPiAjeem4Z387fycqctpsgbf3y6Wz/6ELoMYnhV39KVMjR23e01JvmMPrEdd7hy6P9fNDc/HfNEyV6m/hlWAavdItmRa2ZoYvW837uXnmvO5gWJSFxcXE8//zzrFq1ipUrVzJmzBjOOeccNm5smPJ8yZIlnH766UyYMIHly5ezYsUKbr/9dlT5KUFqJ4+mx/NxzwTWOqzcsiYHczs3+GypGy5II3FUDLu2lbP0hSyqLc5t5FhSspNVH15MWd5qul7/E4P6TnTq8QHOZi8pXp233r5HaDB2TWPJXjlombMpisKl8ZEsH9GH0wK9eWxnMWMXrGVzdZ2rQ5Oc5KTbhISEhDB16lSuu+46hg4dyvjx4/nPf/7T6uPJNiHSyRJCMHllNn/W1HJmSAAf9El2dUgn5OcFu1g7Zw8RQyK4ekwyJkPrH+wOh50Vf72MMXcu/mc8S0pCHydG2tTzs6dzdtd00hPar12Lu3ljRRY7a81MGzXU1aF0aItKq7hzXTYFQuHaiAAeTkvCRyc/5LqLdm0T4nA4+Oabb6itrSUzM5Pi4mKWLVtGREQEw4YNIzIyklNPPZWFCxe29hSS1CqKovDZoIbJ7n4tq2LMvI3cuGoHG9z809PZIxK4+/7BUG3jgzvm8Z93VrFxd0WLj7M1exkb3p2EMPrS96af2zQBgYakr7MPAH99n3RmWhUcmnuXvHm6U0IDWHxqX26LDuLj4koy52fxT4kccdWTtTgJWb9+PX5+fphMJm6++WZmzpxJeno6OTk5ADz55JPccMMN/PHHH/Tv35+xY8eyffv2ox7PYrFQVVXV5EuSnGHDKb24KCiATZqNn6uqGb9iG7MK3fsNy8/bwE0XpnPe1GEMHhjFjLfW8vaPm6kxH79RXlVdJQu+uZPKhW8Qe9nHZI65FVVt+2oSDVCVzv1p1Mto5NYAA6NmLWZvpXwPa0smVeWRtCTmDUkn2qByxYZdTF6xyeWDF0qt0+LqGKvVyu7du6msrOS7777jgw8+YN68eVRUVHDKKacwZcoUnn322QPbZ2RkMGnSJJ577rlmj/fkk0/y1FNPHbFcVsdIzmTTBGcu2Eylw8GS0b2aHe/CHdXb7Hw3eyc7FxeCSUUxqPh5G7j1xj4YjQ3DhQshWL7sW3xWvo9u+N2k9z2zXWP8z9/fckn3PqTG92jX87qj7rOW8kZyBONTPKMK0NMJIfgyr4gns/PRUHisazSTu0Shesjfd0fjknFCxo0bR9euXXnooYdITk7m888/58orrzyw/pJLLkGv1/Pll182u7/FYsFisTS5iPj4eJmESE73Vf4+7tm2hzviwnkkJdbV4bRKndnGVz9uY29+DQ/cMZCiinzqp08mtr4A050rMZr82j2mp//+lsvT+tIttn26GruzG2cvIdfq4PtRgwjwct+B8zqaUqudB9Zu4381Vnob4PW+3enh5+3qsDodl4wTomkaFouFxMREYmJi2Lp1a5P127ZtIyEh4aj7m0ymA11+939JUlu4OLphttPX95S4OJLW8/EycP2lPYlIDOCTZ3+gfOrD1Pteju/dG12SgEDjPDSdvDpmv/fGZGJDYVupe1f7dTShRj0fDkrn296J7LM5GLt8C09uynX73nFSC5OQKVOmMH/+fHbu3Mn69euZMmUKc+fO5YorrkBRFO6//35ee+01vvvuO7Kzs3nsscfYsmUL1113XVvFL0knTK8qjA5oGCL86lXZ1HrAgGZHc/MFaYy6MIOws25Ften57fHPsFW7pi2CEMji70NYFYU9te7dCLqjOjUsiCUj+3FDZADvF1YwbEEWC0srXR2WdAwtmoO6uLiYq666ioKCAgIDA8nIyGDWrFmMHz8egLvuuguz2czdd99NWVkZffr04a+//qJr165tErwktdQ7GUmMXrKZP6pq6DVvPV/160pmiL+rw2qVHr0aqj+iB/dl1b2zqMrJIbRP33aPQwBqp+8f0+DtFVkUKjoC9Z133BRX89KpPNmzK5cm1HPr6i1cuC6XcwO8eD6jG0GGFj3ypHYg546ROqU8s5UbV2SzyWrl33Hh3NItGm8PHW/AUVfNT4//QLc0PemXnIPer32rZR7961tu6D2IhCjZGPP+vxdg1el5dXSmq0ORAE0IPsjdy7M7izAieL57F86LCfOYhumeRk5gJ0ktUOtwMCVrFzMqq/CvsTN+jx0vTUGlYawRVQWFhqoGRW2sclAUVKVxmdK4nQIKjd8b39xUtbF04NBtG4+bV20myEtPsMnQsBClcR0N2zfu17gKhf3naohbUZQj1tnr6tm5ahexYfvoOUCHv3H/n/XBN1vR5I1X4cg//EOWHbJtk+2aLG94/UVhFT6JmZj8Qps5ZjPHaGaZM96GxGE/5NRbSPYxHbgD++/V4cHs/7dp2KbhZ8H+f5XGZYedS9n/bwvoFNArCnpF4dMdeYy01zIqPgadTse7ufnoDt2n8a7tf33wnA1LNIcZS+W3RCpK47LG36fGmvOD51UPxte4XcP1KYec69BrVJp/ffh2h/6sNL/N4cc4Yt0xSsUOvaaWHPNoxxUINBrafWhoaEJDQ0OIhuVrS9cSoA/AovNnh/8EKv170d/Hxvt9+hDrZTzm+aSWk0mIJLXCqopazl65jQih8FpoJJoQCE3g0ARCNHya0oRA0xqWa1rDQ1MToGkN6xDgaPwuBGiIA9sIreHNUtCw766qegKMBgKMOgQHH8BCHPa68YU4sA4ajrT/ddN9NLuN9eW7CQup4dykQxrkiYMPu4MPvoN/9kqzbwGHrD/qtg0P1Bx8WRkxDE1pKOo+2rNEOez70dY3u5E4xo5Ndjt4ldtqzaT6eh3YX7D/qg79/yHfG+91k9OKg9s0vXIO/Ns5hEADNAH1FjPhdTXoafi9UIG7+vQ44hz7/30P/Ny4flPFHt7Y8AZvDLobTWiN6/f/LmkHXu9/yO5/ffg2+8/V9FpEk9eHX21zj4KjHevw/Y68c0c53qExHOPYR8SOaHZdQ/KvoioqOkUHggM/q6hEBETQPf5gr60fC0u4b0sOZqFyf2I4tycmoJOlIk7Tmue3rCCTOr0BQb7cmRDBy3tKiO0RQrKP53atfHx+AHZNY8SI9usuOwS4rN3O1rHVUIWXKZQeMXLMlbZwblQ4Y8JCuH/TJp7bWcHXewv5KCODdH8fV4fWaXlmJbgkOdmV8eEAfJrnud13D3Crsk2pJd7c9CNCyJE/21KAXse7Gb2Z2TeJOgeMXbGFKZu3yO68LiKTEEkCYkwGxvn78u7efbyYXeDqcFqtmSYPkgdZsXsmXnpfVu7b6epQOrzM4EBWDB/MDbF+fFpQy4CFy5lfVuHqsDodmYRIEg2N4j4f0A1/FKbmFRE/O4szF23m1R0F/FZSQZnN7uoQT4iiNrRNkDzT0yNeYmfZKn7evczVoXQKJlXl6e6pzBmcTrBB4eK1O7kmay0VHvL33hHINiGS1EhRFOZkpvFV/j7WlNawx2zlud1FAOgFfJGRzKgw924sraBQXm9l076aA8sMOpWUYFnn7QnSg5NRdX5c3328q0PpVLr7eTM/czDv7t7NsznF9F+0iudTE7koOkJ2521jsneMJB3DrnoLOXUWnticx3arlRdS4vhXY/sRd/TXzlKenb2tybK8vdX8cdtwuslExO2tLy/kwSUv8NsZL7k6lE6r0GLjpnVrWVajZ4Cvg3czehMnu/OeENk7RpKcLMHbRIK3iSGZaQxZvJH7s/Mx6lQuiQl1dWjNGp8Yyvhrmw6UNfKDxbKdiIewaBZq7bWuDqNTizIZ+GnQQH4sLOa+LTlkLlnL/YmR3JYYL7vztgHZJkSSToCPTmXWoO7ogPs35zGn1DXztLSGEAIPHQy200kLiKLGXOrqMCTg3KgI1gwfxOkhRp7NLWX44hVsrpZzAjmbfGuSpBMU42Vk3Sm9CNXruHztDl7M3tswUJmbS40JYPQLc3lt1S5XhyIdh6/BhJdBVkO7C3+9jvf79GFmv2RqHA7GrtzM41u3YdVkd15nkUmIJLVAqFHP0hE9uTA0iKl5xZy+eAslVvce1+HDSb357JahvDNvB5nvLCSnUn6ac1eaEEcdcVZynczgQFYOH8LVUT68n1/DoIXLWVFR7eqwOgSZhEhSC5lUldf7JPFJz0S2my30WbiRXrPXcs2qbL7M38dL2QXk1VtcHWYTp8aFsOL2kUQFenHam4soqnOv+KQGtQ47dtkmxC2ZVJVn09L4a1B3TCqctTqbOzdsoNbhcHVoHk32jpGkk5BXb+HtnCIK6q3MraqhvvFTrEHAj/1TGBDk69oAm3HnHxuZu7EIvbFxajUBdWYbM68bSlpo+87AKzWVW1PGVX/dzYLzPnV1KNIx2DXBy7m5vLK7DH/Fxlu9ujMmLMTVYbmcnMBOklxIEwKbENTYNSYu2UyNprFxVIZHjDMw6asV3DcsmdGJ7tnrp7DoF/Lzv8JoDKNb1wfx9o5zdUhtosBcy5V/3cc/Z73t6lCkE5BbZ+HatevYbDYwMRhe6dmLQEPn7XTamue3rI6RJCdRFQWTqhJq1HNL1yjKEGyqNR91+y/3lnJRVjZTtu2hyu7aIl2dQaXe6p7FymbzXjZuvBtVMVJc/BuLl5zq6pDajB71mDPWSu4lycfEP0MH8nRyKH+X2+i/aBU/FRa7OiyPIpMQSWoDwfqGT0NHa7S6qrKWe7fmoVcUPs7fR+qC9e0Z3hHC/E08+MsG+r8+jz6vzGV+XrlL4zmUouhQFB12R83xN/ZwOlVpMm295P5UReHGhHiWZfahh4+Omzbv5YKVq92+wbq7kEmIJLWBTVV1qEIwMKD5NiH7HzPuUlHz0em9WH/vGFbfcSoZiUHsdKMeNCZTJBkZ72I0hhEbcxnDT1nq6pDajE6WhHisGC8jvw4awCupkayqtjFw0Ro+37NX/nseh0xCJMnJ9pitvJ1XwsTAAPz0uma3GRjoy7Tu8Vg1weSYULYO79XOUR6dEO6THO0XFjqKPhnv0qPH/2Eyue+w+SdLVRUEcgwKT6UoCpfGRrPqlAEMDdBz//ZiJi5fRZ7Z6urQ3FbnbUEjSW3k4Q27sKkwNSPhmNtdGRPKlW44/LtBp/Lcn1t5a2EuikLjl4Lm0LhicAK39e/i6hA7LB2qrIzpAEKNer4d0J9fioq5e3MOw5asZUpSFDcnxKF6QEP19iSTEElyMq2xHOHVHQU82T3OI3rHHOr9Sb0ot9oQGjgEaAgcmuDHbUUszC2VSUgb0qmKLL7vQM6KjGBUaCh3bVzP07mlfLO3kI/69qabj5erQ3MbsjpGkpzs8wFdGe3ny7sFpWTO38ineSXYNc95sOh1KuHeJiJ8TUT7mYj186JLgDch3kZ0qmclVJ5G53YVYdLJ8tfr+LBPX6ZnJFBiszNy2Uaez87F5kHvCW1JloRIkpMpisKXA7vx/d4yXt1RwIPZ+TyYnc8Ef18ywwOYsXsfdgHXJIRzaVwY3h4yu5wmBMs2lzChehlWuwOrXaOqxsrto7txcz9ZOuIMqoKsjumgRoYGs3r4EKZs3sgru8v5vrCEDzJ60ucojdc7CzlYmSS1ISEE7+8qZlV5LX+UV2FRIERRidDp2GK3oRMwOSKYZ3sdu/2IO6izOViwp5wgk54Ak54gk56Xlu3Ey6DjPyNTXB1ehyCEYMRP17Pw3A9dHYrUhlZWVnHd2g0UO7y4Jtqfx1K6esyHkWNpzfNbloRIUhtSFIUbEyMhEWyaYJfZQrK3CVVR2FJbz/u5xXxUUs55FaEMCnL+kOk7d1awaFE+o8YlEBt5csf3Meg4LSmsybKiaguju4YdZQ+ppRRFtgnpDAYGBrByeCb/2baZD/ZW8WvJSt7tlUZmsL+rQ2t3MgmRpHZiUJUmDdJ6+HoztWcXvp1TzvySKqcmIas2FjPnjfV4C4Vab4Uvlhbh0yOQuPQQzh6ZgM5Jn7p2ldbyZUU9v20ppNpsZ+rp6fQO73xvpM5S79CwySSkUzCoCk/3SOfy2FomZ2VxXtYOLgr34tkeKfgfpWt/RySTEElyIVVRMAH7LM4bXfH9V1dRnlNF4JBwrr2iJwaDDovZzpxFeaxfVsTzv+3CGGQkOiEAixCcfWY3woO9W3Wu5yemsaW0lgCTgVnbiznz9YXsenqi066lszE7HPh0gGJ56cT18Pdl8fBhvLRjO6/sruSv0lW80TOVcWFBrg6tXcgkRJJcyKpp1CFI9Gt9l72KSjNLlu+la1IQO3dXYdlcyV2vn4rRcPDTlMlLz+ljkzh9bBKlFWZ+/SUbs8WBWRFMn7KEimA9ftE+nHZGMj26nfhsoJkxwWTGBANwQWok/XPKWn0dUsOYLLLTYuejUxTu75bKxTFmrspaxZXrd3J6iIEX07oTauzYj+mOfXWS5OYWltUgFIVTQo+swhBCIETDKJqHm7swj8UzdyAMCroaO6qqsDzciMOgctn9fZskIIcLDfJi8r8OGaH1WrBZHcyZn8f376wjLC2Yiacn0SVWNgxvb4qsiunUEny8mJs5jPd25fJMbgmDF6/h8z6pDAsOdHVobUYmIZLkQh9sLwAB9dur+LWskNxNZVjr7JjLLSgKCEVBoSEZie8TxvnnpuLnbUBDoNoFV9w7gPiYk2+DYTDqmDAukeGnxDLr7518/s5asGoExvliDDDiUCDcz8SQQdHEx8vkpM0oAiHHCunUFEXhpsRkzo2KY+jSLN7M3c6w4IGuDqvNyCREklyk2u5gtrmeAbsszFuWi8nPQFqfCEJDveiTHoaqHiyWr66xMnPmNl57fBGB3QO54pJ07HbBR2+s5olnnTe1vY+3gfPOSuG8s1KwWB0sXJpPXZUVVUBRtZlP38lCrypEJwZy4SU98PUzOu3ckqyIkQ6K9Gr42woxmFwcSduSSYgkuchbO4sAmHZRBml+x24Y6u9n5Kp/9cJud/DjT9t5+z9LCe0VgtEqEJpAaYORTE1GHWNHHjkIWUFJLX/O380rTy0mOiOUSy/qgY+Xwenn75QEsiREOsBbcWDVHK4Oo03JJESSXGRvpRmAMcu3knVKTyJNx3+Q6/U6LrygB9q5qbw4dTleMT5tkoAcS3S4L5MvSKP+jG7M+GErrzy2CKFXwaFxuhB8ds1rxBX8Q01oEJqfH+e++2a7xufJPGyaIakNaUJQo+kJNHTs0kY5YqokuUitw8FPRRXcszUPvYAFQ3uQ5IETWwkhGj/BNzSiXf/Iq/hlpJF0yQRm3jcFXcHeA9vq6uqwRkcTd/FFDBh5SpMqJwlqbBbO+O1O5p/zrqtDkVzMIQSJc1fR3RvezehFVw94b5AjpkqSB/HV6bg8JpRhwX4MXbqZv4squSHJ/d9oDqcoCjT8t38BNH62OW/ac022rampZe57H1HzxBOUfTedsPCONdpqQ48m0apRT4UQOITWBlFJnkinKLzeI577tuZyytJNPNctnGu6xLs6LKeTSYgkuVhenQWAQc100/VEigIcZYZQPz9fzrznDv4sKmTpHXeh6XQNpSiqglBUhKIgVLXhS1FQVBVFp+I9YCATb7gagH+2bGfBN19Sk5QKtTUN2zQe3+XFukrj/1pRr7LDOouiSJV+caOcHZXkoc6NjuT0iHCuWbuep3YUcGlsbIeYY+ZQMgmRJBd7c0sBsZpKhn/rRi11O6p63JKACf99psnPQgjQNNA0xP7vDgc2TcNsdzDvxlv5ee6cxmofQWZxAQX9BnH5FZdg7CBDXN8yfQX/ybydnnE9XR2K5Ea8dCrPp/Vg6NKNTNmylZfSe6B2oMZDMgmRJBfaWmtmrrWeAL3aod5YaGF1hKIooNOBTtekb4gO8ALO+e7rJtvv/P13Ar77DuM5k046VHfiZk30JDeR4G3i/i5BvLC7iiXly3indy/6BTp/wktX6FjlOpLkYZK8jQSrKlWaxu+F5a4OxykURUEcpTrGWRInTkS1WNg1d26bnkeS3MU9XZP5oW8SVgETV2dz+rKV5NSaXR3WSZNJiCS5kFFV2TSyN14CXtqy9/g7tJC9rAz7vn1OP+4xqSrt0Toj7f+eYe/UqTgcHWQcBUWWhEjHNiw4kBWnDOHJ5DDW18E1a7NcHdJJk0mIJLnYLrMVC3BJF+f2FNn3zjtsP2U424ePYO8jj7TfA64dSkIAAhIT8B4ylLWvvdbm52oPCgqODj4wlXTy9KrCzQlxPJ8Sy1aLF4vLq10d0kmRSYgkudjLm/LxF3BFQrhTj7vvvffxHz8exWCg8vsfcFRUOPX4R6OoSovbhLRW7wcfwDbrT6oKC9vlfG1JPYEGvZK034TG7u1Xr9/u4khOjkxCJMmFlpVX821VFVdHhTq9653fyJFU//knwmYDQOfXTg3Z2qkkBEBnMhF9++2sf/SxdjlfW1IUBU2T44RIJybSZOD5lCiqHCrTduxwdTitJpMQSXKh5xvbgdyQEuX0Y8dOm0r8e+8S9/Zb9Fi3FsXQXvO7tF9JCECXMydhrKsld968djtnW5BJiNRSg4MCAZi2u5oSq83F0bSOTEIkyYW6+zTMkNl70UZiZ2cxbs4Gpu/ZR60TGlsqej1+I0fiP3o0irH95p9oz+qY/Xo88wwFU6dit9vb9bzOZNEsrN6z2tVhSB4k3c+baSnheGHh3JWrsbdTCaQzySREklzouYwEHkuI4kxfP0b7+2JUVe7cvof0uet5fN0uV4fXOu1YHbOff1IS/gMHsuYNz50sb0LiBAprPb9ti9S+royL5du+aeSYDbyck+PqcFpMDlYmSS6kKAq3JUdB8sFlu+otvJtdyHv7yknd5cOVTm6w2uYUBVcMoJ4+ZQorzzmXiksvISjK+dVbbc3b6C17x0itMiQ4gGiDhZUVZUBXV4fTIjIJkSQ3k+Bt4pleXShZYeO+nHx6BfuS4G1ka62ZCpuDMouNwhoL68trqbQ5MGsaZiEwawILArMQWBSBAsSqegb4+2DSq9TaNPxMOrr5e3FxlzB8dW0z3Hl7DFbWHJ3JRMytt7D+iScY8a7nzUJr0puwOqyuDkPyQEIIiu0GxvkGUufQ8FaVhlGIPYBMQiTJDSmKwlsDu/LLvLWcvmrbEeu9NUhQ9YTodATr9fjoVLz1Kj46FR+DDh+DDocm2FBey9zKGuwIvFAoQ6NmHzyeU0AP1UBXHxNj44KZGBmE3wnOwaIJQYXdQbBe1/wbnd2GcFHbjPizz6boq6/Jnj+fbiNHuiSG1gr1CaXa4dljPkiuYdEERkXwWWEtnxWuI05v5v96pHBaWIjbJyMyCZEkN2VQFb7v05V/9pYTFeDFKSH+RBoNBOhVjGrrm3Ptrrfww+5SVu2rZm1NPT9uq4FtecSpOvZpDvYPBN0LPeMigxgRHcjckipezy/BSwOrEGg6BV8N/FAYGejHfT3jSPBuaGRrnv4ZcZ98cvI3oJV6PPMMa++6i4TMTAzt1iPo5IX5hFFlr3J1GJIH8tKpLM3sy8Lyamocdt7KzeHqDXlcErGPe5ITD/xtuiNFuNnoOFVVVQQGBlJZWUlAQICrw5GkDi/PbOW6ldmssx2/KmCwjxdnRAYT7WVkS1ktRfVWfqmooroxJ7pgx06u++xD0n/5Bq9A3zaO/Og2PvY41eFhDL3zTpfF0FJms5krvruC76/83tWhSB5OCMHdGzcyvcSCEApXR/vwcEoK/m0843Rrnt+yd4wkdXLxXkaG+DW8Od0YG8b0Pl15J60Ld4b70Y+DYw/4OWwsrzPzQnYeM7JzGR8bzJXdIvFTDla9zA4PRhEV/PPDdMpsrusu2+PhKai//U5Zgef0NjGZTHKcEMkpFEXhlV69yB7Zj5ti/fm40EzKgvWMXrKUtZU1rg6vCVkSIkkS80vKmbxuB/WqniE6Bz+O6H9EXbIQgrXV9fxv916+LSyjWNcw9oiPw85HfVNI9/chzKgna/Uazqhq+HxzQ6CJ87p2ob8LSkXyZ85k+59/Mertt9r93K113mfnMfOqma4OQ+pgttaaWVxWziu5O6lyqMzP7Ee8l/PHDmrN81smIZIkAWB2aIxYkEWeUFk+NI0ux6hHrnNo/FBUzrKKGuK8jIQpGltKK9hUU8cqYcAoNIaZdCytt2HW6bnOz8A9fboTamy/ZmhCCNZceim+t91O95Ej2u28J+O8z87jh3/94PaNCSXPtM9qZ9iS1dgErB0+gAAnV8+05vktG6ZKkgQ0NG57p083Jq/aQubijQzUa2SEBtErOBBVUVBVhVqrjZzKav4uLiNbPZik6IRGhGYjXFW4McyHu9KSCTHo0YTg7c25PFtQwYeLNhBtNXNdTCi39045ZiybSjfx1JKnKDOXcXmPy7mm1zUtvh5FUUh5+mnWP/EkeEgSYheeO+Kr5P68dQqXxcTw7p59LCmv4rTwYFeHJJMQSZIOGhAcwJJRA/gsN5//7CljWUkNlDStQzZoDkaaDNyeEM3YyBAUlKP22FEVhdvSkzm/q5XvsvcwZ5/G/+2r5cx6C4nHKGn57/L/YnVYCfcO56VVL9EloAtju4xt0bXU7S0gd9589Hv3tmg/V9lWtI1QfWjLSkEcdrBWg7frHyaSeyu22Bi/fB1F9obSjxq7e8w1I5MQSZKa8NfruC2lC9d3jePXwjKGhQYQqNdjFwJvVUVDYGphF+Fok5E7eiYz2e5gwOxVvJa1lZcyM46+vV80m3Ztwq41lAzE+cW1+Dpyv/ySupwdRD74QIv3dYWZWTMZnTD6xHfYvRS+uQLq9kHiCLjyB9C33xxBkuf4Nr+AB7bloUPwQXoiY8OCnT5rd2vJJESSpGaZVJULYsKaWdP69goBeh33dQnniYJKLi6tZGhoYLPbPT70cboFdaPMXMY5Xc+he0j3Fp9rT95ugvr1I2nSpFbH255WFK3ggws+OPEdFr8OXgHQ70pY9ArMmgKTXmyz+CTPUmq1s7m2nt+KCviooI4+Pnq+7p9BiMG9HvvuFY0kSR3e9d0T+XHPcq5btYVn+qQyKTwIg9o0sfEx+HB97+uPeyyhaez47DP2/fgTHDrvioDAqipizj/f2eG3iYKyAhRVIcg36MR3ShgGW35tSEAA+l7RFqFJHuiHgiL+vWU3NgzosXNmiIE3e/dtcQlme5BJiCRJ7UqnKHwyoj8T5izihZUL+at6M2N6T+Dc9D7oTrA9hKZp7Ph2OrWvvgJBwaR/8jEBHjhp3X7vLn2Xs5LOatlOQ2+FyJ5QlgspEyAwtm2Ck9xehc3OXouN13Oy+aeslirhxTB/Hc+mdaeLlwkfN6l6aU6LkpC3336bt99+m507dwLQs2dPHn/8cSZOnNhkOyEEZ5xxBn/88QczZ87k3HPPdVa8kiR1AJEla1m79JwDP5u3TuWf4Eyqw/uzJWYUQ1J7kRYcQIzJ0KShpqZpbPt2OhWff47aM530n37CLzKyzeIUDkHNwnyse2vw6h6Mb3/nn6veWs+K4hU8MuGRlu2oKJA8quFL6pSEEDyxdRvvFdQBCnrsnB3my7lRcYwPC/KIrt4tSkLi4uJ4/vnnSUlJQQjBp59+yjnnnMOaNWvo2bPnge1eeeUVj7h4SZJcJPBgQ1Mx4VlKCnaSuGM+qVtfhq0vc1PhE/wUMYZellrOiQrh9JREArZuIeexRzGkpdH7k0/wjQhv8zBrFuVTOWsnxjg/yqdvw15UR+DEJKee480FbzIhdgIGvefMc9PZrNq4lL/X/o4DDbuwY8eBAwcVohL1WAOPi0NfHmVIrmYWH3Xbw/arNITyd/RlXB7py0UxMfTx93HrUo/mnPRgZSEhIUydOpXrrrsOgKysLM4880xWrlxJdHR0i0tC5GBlktQ5aLuWYf36BrzMu9DQUefVFT/zwRmDf71mPV/mFrPYrmDR6QipriRKp5AWE0lqgB89AnwZERJw4E3Xrgl21FswaxpGRWFjVS1VNjs+Bj0VZgt2Iaix2Uny92VcePAJDZxW+edOahbtxadPOLXLC/HuHUboFWlOuwcWu4Xzvzyf7y79Dm+T9xHrhUOAEKAoCAQaGkIRaEJDExqqomLSOWdyMk3TqKltOoGeclgjZAWloQSGhubJ+9crinLwNcqBtsuHLlOUI18LIRCIg98RNPx3+HLgkG0algOH7dvc8sPjP/gB+eDVHRF/47L9F/L2P6+SHppOerc+6FU9OkWHXtXjY/DBqDbtkXTE5+9DFhzx4Vw57IVy6Kr9cRx9m19LKrl1+17WDutJpMn1SWy7DlbmcDiYMWMGtbW1ZGZmAlBXV8fll1/Om2++SZQH189KktT21IQheD20DiryYOvv6Heuw+zoixIUh2ngxZwZ0YUzE7pQ59BYWFrJ/I1b2aozsLK8ml/La7EoKgHCwd2JUawuq+LPqnosSvOfAvVCoAHeCGqVCkxb87g9NpR7U7ugHqPU1n9kHI4KC5ZdVfidGkfgaYlOuXZNaFz25WXYsDEgZECzCQjADZ9dhU49OKqlioqCgoqCisp8sYxL/c7jj/rZTPQd1/AwVRSEIhiTOp6BaUMPzEdz4AEvxMGfASE0QGHdlpU8uHwKfYw9j4hj//YHH+uHvjq4nmaXNf1k31xisP+7csRPNPl/0zUnts/+teKIKA7GKg6J+PBlAoEZC9eccgNREe7T7mZNZS3v7Nzn6jBOWouTkPXr15OZmYnZbMbPz4+ZM2eSnp4OwN13382wYcM455xzjnOUgywWCxaL5cDPVVVyKmtJ6lSC4lGH3IjXkOZX++hUJkQEMyFi6IFlQghy6i08vTGHp3aVoBOC66OCOS06HD+9ilUT+OlVUn28sGoCk3rw022J1cYLW3byUn4Z22vqebt/96M2iFW99IRc3PLuwceyr2YfV353Jel+6dwy7BaSIo+s3hGaIHfrFmpFHV9PPvqsuoXF+VRWlTO0bjiRhnA0TUNoDgrKCnhxyVQClvsDTUs0Dj6yGziqrOgCTNRp9TyYdh8ThrWwgazU7qrsDiau3g7AfdFhblEK0lotTkK6d+9OVlYWlZWVfPfdd0yePJl58+aRnZ3N7NmzWbNmTYuO99xzz/HUU0+1NAxJkjoxRVHo6uPFp4PSWVRWTaKPidijTMjlpWuaYIQbDUzNSKF/XhF3ZxfQY3se96R2aY+wAfh0yadEG6J56cKXml1fb63nps+vIS4gjjuG/vuYx4qKiCUqIpbu9GqyvBcw/lSZTHQ0dk3wUU4hr+0qxg94slsMVyZEuDqsk3LSbULGjRtH165d8fb25rXXXkM9pB+yw+FAVVVGjBjB3Llzm92/uZKQ+Ph42SZEkqQ2d9vqLfxcUccvA1LpG+jnlGPuKirnzNfmEevTUNUhAPWQaiINO/v8fiIxMpqXT7ufhOCDDWy3ZK9nX1Uxv2z4mf9e9apT4pE6jo9yinh4VwETfX35T0YCcW0wE+7JcMkEdpqmYbFYeOqpp7j++qaDC/Xu3ZuXX36Zs846ekZuMpkwmZzTsEqSJKklXuyTyrqFa5m8ehuLR/TB1wmziv6+cht9w3V8ftexSiIu5NPVf3PZT3fSM7gvL0y4DW9Fx03zb+GcgImc0cMzRnmV2pepsVBvV73F7RKQ1mpREjJlyhQmTpxIly5dqK6u5quvvmLu3LnMmjWLqKioZhujdunShaQk53ZpkyRJcgYvncqnA9MYtWwTD6/P5tV+rW//kZW9h2d+WkNWicbHV/Q67vaT+4/jX33HMG3hTCZ+cy216mYuDDiLey5s4XghUqdxRlwo9+4sYJNmx+zQ8PKw7rjNaVESUlxczFVXXUVBQQGBgYFkZGQwa9Ysxo8f31bxSZIktalkXy/uiAvjlT2l3FNvIeEYs/sezRs/LmTa0kr+PTiIL+4Yisl4Yg0FVVXlgZEXcOuQScx48S1CE8YhhGizcZaEENSUl+ITEIhOjkvicZaWN8xo7SWgtoMkISfdJsTZ5DghkiS1t0qbne4LN/B0YgQ3JsW0aN+Csiouef0ffrznNEL8fVodg9AEiz9cS2GNhTNuHYC3ybmzaljq6vju/x6hcMd2dAYDFz/+LDGpzhvzRGp7q6tqOWPVdqamxvGv2OYml3St1jy/PT+NkiRJOgm1DgejlmwEIKWFSURhRS0TXprLY2emn1QCAqCoCqfc0JfEbsHMmbaM4tK6kzre4XLWrKBwx3bG33A7DpuNrx+736nHl9pehNGAIiCvxuzqUJxGTmAnSVKnUV5rZWtRNQadgo9Rj0MT/FlaQYFd4464UEaHBR1zf00I5pVVMzTID2+dyvt/ZpEW7sX4AUdvS1LrcPBcTgEbqusZFxrAbV0ijlndMuCsVHZE+7Hp9dWsS/Qnrn8UPTJOfs6a6G7dMXp789f7bzSc58zzTvqYUvuK8zJynp8fX+eXcl9KDEY3nBW3pWQSIklSh2e2OXhi5iK+XV3d7HqTj473gsqYs2Evw1PDuSklnmjTwd4HZodGVnUdX+Tv47viigPLdZrKgyMSjtmO463dxXy5t4wxof78X04BO+utTOsRf8x4uw6MISIlhK1ri9j9/TanJCFBkVFc/eLb7N6wlrD4BCKTu530MaX2lxkVyA87aqiwOYgwySREkiTJ7f3fz4v5PquSh05LYlzPBBwa1FntGHQqxVX1vLt4HVmFZrKXm9m+vYr3YwqJVQVdfA3sC9KTranYm0kyQqJ8ebaynt+WbOGbQSkEGY58S9U1zvtidjQ0vzOqJ9bo1D/Qi4EjE5i3MJ/Z767GJ8Sb8O4hdD2JhMQ/NIyep45t9f6S68U0ds39bEch96UfO5n1BLJhqiRJHd5V7/+Pomo7f9x99jGrQuZu2cv//bqC7H069g9sbvRWuO3S3oyNCSXV1wuFhqRCryioisJvReXcvGEXvorCq70TmBAe1OSYZofGtJ2FDdUxYQFcFxvWot4vVZVmdmSXsa+wBvuWchRN0PfydKJi5ftjZ7O2opaJq7ejKfBWtzjOj3evxqmteX7LJESSpA7vr417uOHztZzVy5sXLx2FUX/sYmyzzcG2omqm/bGK+dlmuoVpzLz9dPy9mu/WurPOzJWrdpBttzHU25un0+PICPBti0thc1Yh6jfbMF3fi8RuIW1yDsk9PbhxF58WlzPUy4sfM3u4OpwjyN4xkiRJzRjfM44XL+zOH5tqOP+N31iUXYJDO/rnLy+Djoy4ID67fiwzbhpK9j6VEf/9B6tda3b7RB8vFgxP57nkGDbXmZmwajtjFm4ku9b5vRjy1xaRG+9Dl+Qgpx9bcm/ehoYRfS9PDD/Olp5DJiGSJHUKFwzsxufXDaGy3s4VHyyn68O/8dCMucfdb1BSKHoVKuodnPXar+wqrW12O0VRuCYhgo2jMng1NY4Cq53hyzaTMWcdk1dms6SsmpMteP7nq/XYK61MuKV/k3m6pM6hqKZhnrWkVgyo565kdYwkSZ2KEII1uyu47P3FhHhbWfLIiXVVzdpdxi2fz8PXpOPPe85CPU4D02q7gx8KyvinsIJl1XVUKoIYRccrvRMZGerf4rhnz9iEfU8N4/49UCYgndQZ8zZSKBwsG9kbwwk2cG5PsjpGkiTpOBRFoX9CMM+e24OCaiPztxWe0H59u4Tw0qXDyd6nY1lu6XG399frmBwfzmeDUtg8OoNvMpIp0hxcvG4HYxduYo/ZesIxL/o9G/vOKsbeIROQzmphSSWrNRt7hcZus+X4O3gI+dssSVKndG7/ZAZ1Ubnuk+W8+tca8ivqj1tdoomGT5+Wo7QNORpVURgVGsCOUzN4KSWOPKuNYYs28cPeshPav35+Pt56Pdm/57Lt15yGr//lsvXXHGoKaloUi+RZhBC8vW0v/1qfS1dFx18DUunq4+XqsJxGVsdIktRpWewOnvpxMdNXV2DXVLz0DoK8HZh0EOGvY3hqHAMSuyAQLN6Wy9sLSgDY+n+nY9LrWn3eCpudq1ftYGl9PVdHBPN8z4Rjbv/SlPkMSAjE388I+9+xBWxcVYROVeh/ahyJI2LwCvVudUySe3pmw25eLynjPB9fHsnoQpwbtweRXXQlSZJaoazWyqpdZWQXFlNWW0O91UZOSTWr92iY7Q3Jho/Bzpm9A3lo0jBCfI3HOeLxOYTghqwcfquoxltAlE7P/akxnBMVjE5R+PXPHHbvqKCyuA5dlZ3b/nMKvj5HdhEuza4gd9Fe8rdXoNk1oqJ9SRkdR1hGx+lB0Vn9UVjO1Zt38WhsBLentmxiRVeQSYgkSZIT2Rwa+eX1qIpCbLA3Oic3BhRC8L/iCn7bW8766jq2O+zEqTp+HpTKR48tZti5XUnqEkBSQtCJxWuxk7eymGXTt+Hlc+jorQ1xK0rDRHmqqqACem8dqqqgKArNXpqiHCx5aYi46eoTikoBRNNtlf3flMMXHboLQnPgZSuF6iL8QkJRdA0JoXIgDEGzT7DDFormruHw/Zo50BGLmjvZEbEcPP6Bx6s4ZFnjelXTYdAM2NSDbYOsqsrmACMr/A0YgLlRAeh9jCwZ27dFA9y5ikxCJEmSPNiy8hquWJONYhOcu9PBCzcOOOmHjxCi4bkoBMIhcNg0NIfAVm3BYXagORp+1g4fN+Xw8+5frew/bjMnU5q+PHQbQZMfDh5Dacg4jnzgQ97W3WSv2cqg8fH4BoWg6g5WgSmHZE0H7pFySFqjKIfcO6UhIGX/tgeyoAPXqTSsbDz2wYtRFOWQ6zrkmMph591/PkVBUdXGn1UU9ZDl+39WG16rRj2K7uB1ZM5eQa5iwEezowHxwsFrA9PpF9Ty3lSu0Jrnt5w7RpIkyU0MCfZj9tA0Tl+8mc9T9Syeu4En0uMZHxHY6mTk4ENUQdGBamx4kJsCTr5Kqa3VmPdh2O5Dyujhrg6lXVwSG87zeyt4JS2Bs6JbNry/p5K9YyRJktxIFx8TWWMy+KpXEv6qjqs27eSsBZvZVef80VfdnU6vHllC04H9OzWBEIeNv/IKOkUCAjIJkSRJcjtGVWVMeCC/j0zjo7QE8mw2Ri/ZwvySKleH1q5Ugw7Rst7QHsuiaVy9dB1lOgMTu0S7Opx2I5MQSZIkN6UoCmdEBTN/ZE96mUxcsS6Hv4sqXB1Wu9EbdGiOzlESUmFz8Gd9Q8Y1v2AfJVabiyNqHzIJkSRJcnOBBj3TM7sz2MuLqzfs5PfCcleH1C50Bl3zDWA7oEiTgc97dmGoXuOTSgsPrtro6pDahUxCJEmSPICXTuXrzFRG+Hhz/aZd/HqCo616Mp1ej9ZJqmMAxkWG8lCvFAAq1CPHhOmIZBIiSZLkIYyqymdDUhls9OL6rbv5eneJq0NqUzq9DjpRw1SAocH+RGk2guwnPreQJ5NJiCRJkgcxqAofDenG6T6+3L0jn/9syDvunDeeSqfXoXXQazsWhxD4GzrHCBoyCZEkSfIwwQY9Hw/uxr1R4bxZUsoHOUWuDqlNqHoddKLqGIA1lTWU6IwMiYpwdSjtQiYhkiRJHkhRFO5Pi+WywECe2lnIxqo6V4fkdDq9vtM0TIWG0W1vXrmJbsLGhXGdY+4fmYRIkiR5sOf7JhCtqDyYtdPVoTidoogOW9XUnD0WG7tUI5regEEOViZJkiS5O5Oq8kRaHCsdVhaWVbs6HKf69KG/iUnxjHlTnCHWZGC0n5EcBzy/ZSc/FJTiaEzCOmoyJiewkyRJ8nBCCE6ZuwGjovD3yJ7onTzbr6t8cM8Mrn/pIleH0e4uWpjFgsaxyrpqViL1KivtCgYhCMHBjYnR3NCti2uDbEZrnt+yJESSJMnDKYrCq/2S2CLs/FjQ8ccP6ejeHdKLpxPCeCUlhhi9ik3TuCEigJtjgkn1MfFYXhmrK2tdHaZTdI4+QJIkSR3coCA/ugodv+4q5cLYUFeHI52EEIOeG5PjALg0rmkvGYcQDPxnBWes3s5/4oK5ISXBFSE6jSwJkSRJ6iBGh/qzuL6uw7YfkECnKDydlgjAr/nFrg3GCWQSIkmS1EGMiwuhSoUtteYmy235+ex9+BH23HEndStWuCg6yVn6hAQCEOTn6+JITp5MQiRJkjqIIUF+GDSYU1jRZHn+gw9Su2gRNfPmsetfV2HJyXVNgG6mal89xbuq0DxsaPgu3ibG++hYWlHj6lBOmmwTIkmS1EHML6nEpkKtzdFkubDZULxMKF5eCKsVHHYXReg+1s3JY8G32wEw+eiZ/NwpGEw6F0d14vyFRiielTw1R5aESJIkdQBlNjuTN+9CFXBJYtPRNmOeew5TSgpeaWnEvf0WppQUF0XpPjYtKiAyKYD49BAsdXZy13rGZIBWTePd7Dx+q7HR3cfk6nBOmiwJkSRJ6gAMioKvBnpVwV/f9BO9KTmZ+DfecFFk7qnH0CgWfZcNgKIqJGaEuTii46t3aExasJpNQs95ASae75fm6pBOmkxCJEmSOgB/vY7vB6Zw+urtzCqq5NI42U33WPqO60JCr1DMNTYikwJQde5fMfDl7kI2CT0/9k1maHDHGMxTJiGSJEkdRC9/HwDeyyuWScgJCI5yr94lVk3j0aythHoZSQ8NJtbLhKIorKms4Y3sPPbSUMLVzdfHxZE6j0xCJEmSOgitsaHiVR1gsLKaihqEw+DqMFpNE4Iah0a13UGV3XHIazsV9WYqzBYqLFYqLVaqbXaq7A5y7II9qgG1woxW1HQeIJ1QGeylcmmXKMKMHefR3XGuRJIkqZMzKAr+GswtrqKoxorZoXFxYhhp/p73ybmmrArvAMfxN3QyTQjqHBrVDgdVdo0au6Phtc1BpcVChdlCudlMlcVGlc1Old1OtUOjVhPUCKhDoQ4Vs+7oj1dFCLw0Bz5o+CLwVcBPVUg1qvw3NZ4xESGU2x3sNVspsNhI9/Mm2mRA7YAz68okRJIkqYNQFIWxfn78WF3DH9U1+GvwTkkZT8ZHclNKtKvDaxGhabTmmWvXRGMC4aCysRSiwman3GyhrL6e8noL5VYblVYbVXYHVQ6NagE1QqFeUalXdYhjnNhLs+MtmiYP/jqVGJOeAIOOQJORQJOJYG8vgkwm/PW6g186FX+9Dl+detyEIsSgJ8Sgp1cHn0RYJiGSJEkdyIO94/DfVsjdPWIIM+p5aN0unsorIjMygIwA92oD0Ry7JqhyONhtc7DH38SCsmoqbHbK6uspqzdTbjZTabFR0ViFUaVp1GhQg0KtojtmCYSXZsdXaPgh8FchQFWJ8zIQZDAQaDIQZDIR5GUiyMuLAIP+QNKwP4Hw0+vQdcDSCFdShJtNMtCaqYAlSZKk5tk1Qd856xjh78vbg7u1+fmEENRpGpU2BxV2BxU2B5V2O2VWO2X1NsrqbZRb7FRa7Q0lFQ4H1ZpGjRDUKgLzMTqpeGl2/BqTCD9VIUCnEqjXEWQ0EGQyEuztRai3F0EmI4F6HQF63YHv/jodelUmEG2pNc9vWRIiSZLUgelVhRiDgZm1Nbzdgv3MDo1Ke0MiUWmzU2F3UG61U1pnY5/ZSrnFToXVTrmtIZGo0jSqEdQoAsdRnvVeGvgJBT9FwU9VCdDpiDcZCTLqCTLpCfYyEOptIMikl0lEJyGTEEmSpA7u3Jhg1uUV8UdJJTV2B6V1VkrrbZRZ7A3tI+wOKu0aVZqjIZFAYD1KiYSxMZHwVxT8VZVAnY6kxkQixKQn1NtImLeBYC8DQXodQQYdgfqGpMIgkwjpMDIJkSRJ6uBGRwXxdF4RV29omLhOJ8BPgD8q/opKkE5HrMlAT4M3wSY9od4GwryNhHjpCdLrCDQ0ftfr8PKAQb0kzyGTEEmSpA4uzc+b9af0xKoJggw6fFQVRTawlNyATEIkSZI6gXCj5w78JXVcslxNkiRJkiSXkEmIJEmSJEkuIZMQSZIkSZJcQiYhkiRJkiS5hExCJEmSJElyCZmESJIkSZLkEjIJkSRJkiTJJWQSIkmSJEmSS8gkRJIkSZIkl5BJiCRJkiRJLiGTEEmSJEmSXEImIZIkSZIkuYRMQiRJkiRJcgm3m0VXCAFAVVWViyORJEmSJOlE7X9u73+Onwi3S0Kqq6sBiI+Pd3EkkiRJkiS1VHV1NYGBgSe0rSJakrK0A03T2Lt3L/7+/iiK4upwmlVVVUV8fDx5eXkEBAS4OpwOR97ftiXvb9uR97Ztyfvbtk72/gohqK6uJiYmBlU9sdYeblcSoqoqcXFxrg7jhAQEBMg/hDYk72/bkve37ch727bk/W1bJ3N/T7QEZD/ZMFWSJEmSJJeQSYgkSZIkSS4hk5BWMJlMPPHEE5hMJleH0iHJ+9u25P1tO/Leti15f9uWK+6v2zVMlSRJkiSpc5AlIZIkSZIkuYRMQiRJkiRJcgmZhEiSJEmS5BIyCZEkSZIkySVkEnIczzzzDMOGDcPHx4egoKAj1q9du5bLLruM+Ph4vL29SUtL49VXXz3q8RYtWoRer6dv375tF7SHcMa9/eGHHxg/fjzh4eEEBASQmZnJrFmz2ukK3Juzfnfnzp1L//79MZlMdOvWjU8++aTtg/cAx7u/AHfeeScDBgzAZDId9W9+1qxZDB06FH9/f8LDw7ngggvYuXNnm8XtCZx1b4UQTJs2jdTUVEwmE7GxsTzzzDNtF7iHcNb93S87Oxt/f/+jHutYZBJyHFarlYsuuohbbrml2fWrVq0iIiKCL774go0bN/LII48wZcoU3njjjSO2raio4KqrrmLs2LFtHbZHcMa9nT9/PuPHj+e3335j1apVjB49mrPOOos1a9a012W4LWfc39zcXCZNmsTo0aPJysrirrvu4vrrr5eJHse/v/tde+21XHLJJc2uy83N5ZxzzmHMmDFkZWUxa9Ys9u3bx/nnn98WIXsMZ9xbgH//+9988MEHTJs2jS1btvDzzz8zePBgZ4frcZx1fwFsNhuXXXYZI0aMaF0wQjohH3/8sQgMDDyhbW+99VYxevToI5Zfcskl4tFHHxVPPPGE6NOnj3MD9GDOuLeHSk9PF0899ZQTIusYTub+PvDAA6Jnz55NtrnkkkvEaaed5swQPdqJ3N+j/c3PmDFD6PV64XA4Diz7+eefhaIowmq1OjlSz3My93bTpk1Cr9eLLVu2tE1wHcDJ3N/9HnjgAXHllVe26H3mULIkpA1UVlYSEhLSZNnHH39MTk4OTzzxhIui6hiau7eH0jSN6urqY24jHd3h93fJkiWMGzeuyTannXYaS5Ysae/QOqQBAwagqioff/wxDoeDyspKPv/8c8aNG4fBYHB1eB7tl19+ITk5mV9//ZWkpCQSExO5/vrrKSsrc3VoHcbs2bOZMWMGb775ZquP4XYT2Hm6xYsX8+233/K///3vwLLt27fz0EMPsWDBAvR6ectbq7l7e7hp06ZRU1PDxRdf3I6RdQzN3d/CwkIiIyObbBcZGUlVVRX19fV4e3u3d5gdSlJSEn/++ScXX3wxN910Ew6Hg8zMTH777TdXh+bxcnJy2LVrFzNmzOCzzz7D4XBw9913c+GFFzJ79mxXh+fxSktLufrqq/niiy9OajLBTlkS8tBDD6EoyjG/tmzZ0uLjbtiwgXPOOYcnnniCCRMmAOBwOLj88st56qmnSE1NdfaluJ32vLeH++qrr3jqqaeYPn06ERERJ3spbsmV97czaKv7ezSFhYXccMMNTJ48mRUrVjBv3jyMRiMXXnghooMNZt3e91bTNCwWC5999hkjRoxg1KhRfPjhh8yZM4etW7c67Tzuor3v7w033MDll1/OyJEjT+o4nfJj+b333svVV199zG2Sk5NbdMxNmzYxduxYbrzxRh599NEDy6urq1m5ciVr1qzh9ttvBxr+OIQQ6PV6/vzzT8aMGdPia3BX7XlvD/XNN99w/fXXM2PGjCOqDzqS9r6/UVFRFBUVNVlWVFREQEBAhywFaYv7eyxvvvkmgYGBvPDCCweWffHFF8THx7Ns2TKGDh3qtHO5Wnvf2+joaPR6fZMPf2lpaQDs3r2b7t27O+1c7qC97+/s2bP5+eefmTZtGtDQE0nTNPR6Pe+99x7XXnvtCR2nUyYh4eHhhIeHO+14GzduZMyYMUyePPmI7l8BAQGsX7++ybK33nqL2bNn891335GUlOS0ONxBe97b/b7++muuvfZavvnmGyZNmuS0c7uj9r6/zVUN/PXXX2RmZjotBnfi7Pt7PHV1dahq0wJpnU4HNHxY6Uja+96ecsop2O12duzYQdeuXQHYtm0bAAkJCe0WR3tp7/u7ZMkSHA7HgZ9/+ukn/vvf/7J48WJiY2NP+DidMglpid27d1NWVsbu3btxOBxkZWUB0K1bN/z8/NiwYQNjxozhtNNO45577qGwsBBoeCMJDw9HVVV69erV5JgRERF4eXkdsbyzOdl7Cw1VMJMnT+bVV19lyJAhB7bx9vYmMDDQJdflLpxxf2+++WbeeOMNHnjgAa699lpmz57N9OnTj9kup7M43v2FhvETampqKCwspL6+/sA26enpGI1GJk2axMsvv8zTTz/NZZddRnV1NQ8//DAJCQn069fPRVfmes64t+PGjaN///5ce+21vPLKK2iaxm233cb48eM7RdX4sTjj/u4vVdpv5cqVzT7vjqvF/Wk6mcmTJwvgiK85c+YIIRq6LzW3PiEh4ajHlF10Gzjj3p566qnNbjN58mSXXJM7cdbv7pw5c0Tfvn2F0WgUycnJ4uOPP273a3FHx7u/Qhz99zM3N/fANl9//bXo16+f8PX1FeHh4eLss88Wmzdvbv8LciPOurf5+fni/PPPF35+fiIyMlJcffXVorS0tP0vyM046/4eqrVddBUhOljrJ0mSJEmSPEKn7B0jSZIkSZLrySREkiRJkiSXkEmIJEmSJEkuIZMQSZIkSZJcQiYhkiRJkiS5hExCJEmSJElyCZmESJIkSZLkEjIJkSRJkiTJJWQSIkmSJEmSS8gkRJIkSZIkl5BJiCRJkiRJLiGTEEmSJEmSXOL/AWUUk/iQfdGfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block reads in a csv with whole districts to be cut out of the map\n",
      "For CA my input file says to cut out 0 districts out of 52\n",
      "There were no cut districts in CA\n"
     ]
    }
   ],
   "source": [
    "print(\"This code block reads in a csv with whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "                            if STATE == \"NC\":\n",
    "                                if t == 1828:\n",
    "                                    print(\"special for NC - include vtd 1828 from county 55 in the cut list for contiguity\")\n",
    "                                    cutList.append(t)\n",
    "                                    cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52 districts\n"
     ]
    }
   ],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]\n",
    "print(nDistricts,\"districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before squishing in outcroppings.\n",
      "Of the total statePop 39538223.0 we ignore 0.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 18 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 52 rather than original 52\n",
      "Each district will be drawn to house 760350.442 = 1/ 52 of non-cutout state pop 39538223.0 vs original= 39538223.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "here is the CA map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before squishing in outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "nCutCountyTracts = [0 for c in range(nCounties)]\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "        nCutCountyTracts[countyNo[t]] += 1\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        if nCutCountyTracts[c] > 0:  #need to modify county shape\n",
    "            \n",
    "            countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "            for t in countyTractList[c]:\n",
    "                countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 8 #11  #reduce for TX\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 5 #7 #reduce for TX\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "the max LAT-scaled distance between counties is 11.26014\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 11.26014  6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "Working on county  20 neighbors\n",
      "Working on county  40 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For corners and outcroppings, we fuse county clusters\n",
    "collapsedCountyList = list()  #VA has independent cities that we will reassign to parent counties\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b562690-94e0-4846-9dba-f7b48925951c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f9b3df93-d821-44ca-b4f4-b65d13baaffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.25 of 760350\n",
      "checking if county 12 with pop 179702.0 and 1 or 2 neighbors is less pop than 190087\n",
      "checking if county 20 with pop 262321.0 and 1 or 2 neighbors is less pop than 190087\n",
      "checking if county 7 with pop 27743.0 and 1 or 2 neighbors is less pop than 190087\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [12]\n",
      "1   [7, 11, 52]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 760350.442 190087\n",
      "You may suggest [ [12], [7],[46], [24,17] ] \n",
      "let's decide whether to delete, accept, or modify list [12] with pop 179702.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's decide whether to delete, accept, or modify list [7, 11, 52] with pop 180318.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [7]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [7, 11, 52] with [7]\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if you want to manually add another corner-county cluster 1\n",
      "Type in a [county list], where the corner county is first in the list.  Or type 0 to stop [46]\n",
      "enter 1 if you want to manually add another corner-county cluster 1\n",
      "Type in a [county list], where the corner county is first in the list.  Or type 0 to stop [24,17]\n",
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "179702.0 [12]\n",
      "27743.0 [7]\n",
      "44076.0 [46]\n",
      "41430.0 [24, 17]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in set(has1neighborCs).difference(set(collapsedCountyList)):\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in set(hasFewNeighborCs).difference(set(collapsedCountyList)):\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "print(\"You may suggest [ [12], [7],[46], [24,17] ] \")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15207\n",
      "We defined a total of 3 unit (but not corner-cluster) counties in the map\n",
      "excluding the cut-out districts and collapsed counties\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in set(uncutCountyList).difference(set(collapsedCountyList)):\n",
    "    if countyGeom[c].intersects(MAPexterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop and c not in allFusedCounties:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map\")\n",
    "print(\"excluding the cut-out districts and collapsed counties\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented basic units?\n",
      "6 fragmented VTDs (units) out of 9129\n"
     ]
    },
    {
     "data": {
      "image/png": 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3IL4rn88nl8ulxsZGRUVF9fdwAADo1rBF6/p7CEHbt+TEl6EEq6/ev/ltLAAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgtaDDzubNmzVt2jQlJCTI4XBo7dq1JzymvLxcF198sZxOp0aMGKHi4uIONYWFhRo2bJjCw8OVlpam7du3Bzs0AACADoIOO83NzUpJSVFhYeFJ1dfU1Gjq1Km6+uqrVVVVpYULF+qmm27Shg0b/DVr1qxRbm6uFi9erJ07dyolJUUZGRk6dOhQsMMDAAAI4DDGmB4f7HDopZdeUlZWVpc1d911l9atW6fq6mp/28yZM9XQ0KDS0lJJUlpami655BL97ne/kyS1t7crMTFR//7v/65FixadcBw+n08ul0uNjY2Kiorq6XQAAOgTwxat6+8hBG3fkqmnvM++ev/u9Wt2Kioq5PF4AtoyMjJUUVEhSWptbVVlZWVATUhIiDwej7/m21paWuTz+QI2AACAzgzo7Sfwer2Ki4sLaIuLi5PP59Nnn32mTz/9VG1tbZ3WvP/++532WVBQoPvvv7/XxvxtJPC+wTr3DdYZ3eH1ARudkXdj5eXlqbGx0b/t37+/v4cEAABOU71+Zsftdqu+vj6grb6+XlFRUYqIiFBoaKhCQ0M7rXG73Z326XQ65XQ6e23MAADAHr1+Zic9PV1lZWUBbRs3blR6erokKSwsTBMmTAioaW9vV1lZmb8GAACgp4IOO01NTaqqqlJVVZWkr24tr6qqUm1traSvPmKaM2eOv/6WW27Rhx9+qDvvvFPvv/++fv/73+u5557TL37xC39Nbm6unnjiCa1atUrvvfee5s+fr+bmZmVnZ3/H6QEAgO+7oD/G2rFjh66++mr/49zcXEnS3LlzVVxcrLq6On/wkaSkpCStW7dOv/jFL7R8+XJdcMEFevLJJ5WRkeGvmTFjhg4fPqz8/Hx5vV6lpqaqtLS0w0XLAAAAwQo67Fx11VXq7qt5Ovt25Kuuukq7du3qtt+cnBzl5OQEOxwAAIBunZF3YwEAAJwswg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLUehZ3CwkINGzZM4eHhSktL0/bt27usveqqq+RwODpsU6dO9dfccMMNHfZPmTKlJ0MDAAAIMCDYA9asWaPc3FwVFRUpLS1Ny5YtU0ZGhnbv3q3Y2NgO9S+++KJaW1v9j48ePaqUlBRdf/31AXVTpkzR008/7X/sdDqDHRoAAEAHQZ/ZWbp0qebNm6fs7GyNGTNGRUVFioyM1MqVKzutHzRokNxut3/buHGjIiMjO4Qdp9MZUDdw4MCezQgAAOAbggo7ra2tqqyslMfj+bqDkBB5PB5VVFScVB9PPfWUZs6cqbPPPjugvby8XLGxsRo1apTmz5+vo0ePdtlHS0uLfD5fwAYAANCZoMLOkSNH1NbWpri4uID2uLg4eb3eEx6/fft2VVdX66abbgponzJlilavXq2ysjI9/PDD2rRpkzIzM9XW1tZpPwUFBXK5XP4tMTExmGkAAIDvkaCv2fkunnrqKY0bN06TJk0KaJ85c6b/73Hjxik5OVk/+MEPVF5ermuuuaZDP3l5ecrNzfU/9vl8BB4AANCpoM7sxMTEKDQ0VPX19QHt9fX1crvd3R7b3NyskpIS3XjjjSd8nuHDhysmJkZ79+7tdL/T6VRUVFTABgAA0Jmgwk5YWJgmTJigsrIyf1t7e7vKysqUnp7e7bHPP/+8Wlpa9NOf/vSEz/Pxxx/r6NGjio+PD2Z4AAAAHQR9N1Zubq6eeOIJrVq1Su+9957mz5+v5uZmZWdnS5LmzJmjvLy8Dsc99dRTysrK0nnnnRfQ3tTUpDvuuENbt27Vvn37VFZWpunTp2vEiBHKyMjo4bQAAAC+EvQ1OzNmzNDhw4eVn58vr9er1NRUlZaW+i9arq2tVUhIYIbavXu3tmzZotdee61Df6GhoXrrrbe0atUqNTQ0KCEhQdddd50efPBBvmsHAAB8Zz26QDknJ0c5OTmd7isvL+/QNmrUKBljOq2PiIjQhg0bejIMAACAE+K3sQAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1XoUdgoLCzVs2DCFh4crLS1N27dv77K2uLhYDocjYAsPDw+oMcYoPz9f8fHxioiIkMfj0Z49e3oyNAAAgABBh501a9YoNzdXixcv1s6dO5WSkqKMjAwdOnSoy2OioqJUV1fn3z766KOA/Y888ogee+wxFRUVadu2bTr77LOVkZGhzz//PPgZAQAAfEPQYWfp0qWaN2+esrOzNWbMGBUVFSkyMlIrV67s8hiHwyG32+3f4uLi/PuMMVq2bJnuueceTZ8+XcnJyVq9erUOHjyotWvX9mhSAAAAxwUVdlpbW1VZWSmPx/N1ByEh8ng8qqio6PK4pqYmDR06VImJiZo+fbreeecd/76amhp5vd6APl0ul9LS0rrss6WlRT6fL2ADAADoTFBh58iRI2praws4MyNJcXFx8nq9nR4zatQorVy5Ui+//LL++Mc/qr29XZdddpk+/vhjSfIfF0yfBQUFcrlc/i0xMTGYaQAAgO+RXr8bKz09XXPmzFFqaqquvPJKvfjiixo8eLD+8Ic/9LjPvLw8NTY2+rf9+/efwhEDAACbBBV2YmJiFBoaqvr6+oD2+vp6ud3uk+rjrLPO0vjx47V3715J8h8XTJ9Op1NRUVEBGwAAQGeCCjthYWGaMGGCysrK/G3t7e0qKytTenr6SfXR1tamt99+W/Hx8ZKkpKQkud3ugD59Pp+2bdt20n0CAAB0ZUCwB+Tm5mru3LmaOHGiJk2apGXLlqm5uVnZ2dmSpDlz5uj8889XQUGBJOmBBx7QpZdeqhEjRqihoUG/+tWv9NFHH+mmm26S9NWdWgsXLtRDDz2kkSNHKikpSffee68SEhKUlZV16mYKAAC+l4IOOzNmzNDhw4eVn58vr9er1NRUlZaW+i8wrq2tVUjI1yeMPv30U82bN09er1cDBw7UhAkT9Oabb2rMmDH+mjvvvFPNzc26+eab1dDQoMmTJ6u0tLTDlw8CAAAEK+iwI0k5OTnKycnpdF95eXnA40cffVSPPvpot/05HA498MADeuCBB3oyHAAAgC7x21gAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGo9CjuFhYUaNmyYwsPDlZaWpu3bt3dZ+8QTT+iKK67QwIEDNXDgQHk8ng71N9xwgxwOR8A2ZcqUngwNAAAgQNBhZ82aNcrNzdXixYu1c+dOpaSkKCMjQ4cOHeq0vry8XLNmzdIbb7yhiooKJSYm6rrrrtOBAwcC6qZMmaK6ujr/9uyzz/ZsRgAAAN8QdNhZunSp5s2bp+zsbI0ZM0ZFRUWKjIzUypUrO63/05/+pJ///OdKTU3V6NGj9eSTT6q9vV1lZWUBdU6nU263278NHDiwZzMCAAD4hqDCTmtrqyorK+XxeL7uICREHo9HFRUVJ9XHP/7xD33xxRcaNGhQQHt5ebliY2M1atQozZ8/X0ePHu2yj5aWFvl8voANAACgM0GFnSNHjqitrU1xcXEB7XFxcfJ6vSfVx1133aWEhISAwDRlyhStXr1aZWVlevjhh7Vp0yZlZmaqra2t0z4KCgrkcrn8W2JiYjDTAAAA3yMD+vLJlixZopKSEpWXlys8PNzfPnPmTP/f48aNU3Jysn7wgx+ovLxc11xzTYd+8vLylJub63/s8/kIPAAAoFNBndmJiYlRaGio6uvrA9rr6+vldru7PfbXv/61lixZotdee03Jycnd1g4fPlwxMTHau3dvp/udTqeioqICNgAAgM4EFXbCwsI0YcKEgIuLj19snJ6e3uVxjzzyiB588EGVlpZq4sSJJ3yejz/+WEePHlV8fHwwwwMAAOgg6LuxcnNz9cQTT2jVqlV67733NH/+fDU3Nys7O1uSNGfOHOXl5fnrH374Yd17771auXKlhg0bJq/XK6/Xq6amJklSU1OT7rjjDm3dulX79u1TWVmZpk+frhEjRigjI+MUTRMAAHxfBX3NzowZM3T48GHl5+fL6/UqNTVVpaWl/ouWa2trFRLydYZasWKFWltb9a//+q8B/SxevFj33XefQkND9dZbb2nVqlVqaGhQQkKCrrvuOj344INyOp3fcXoAAOD7rkcXKOfk5CgnJ6fTfeXl5QGP9+3b121fERER2rBhQ0+GAQAAcEL8NhYAALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsFqPwk5hYaGGDRum8PBwpaWlafv27d3WP//88xo9erTCw8M1btw4rV+/PmC/MUb5+fmKj49XRESEPB6P9uzZ05OhAQAABAg67KxZs0a5ublavHixdu7cqZSUFGVkZOjQoUOd1r/55puaNWuWbrzxRu3atUtZWVnKyspSdXW1v+aRRx7RY489pqKiIm3btk1nn322MjIy9Pnnn/d8ZgAAAOpB2Fm6dKnmzZun7OxsjRkzRkVFRYqMjNTKlSs7rV++fLmmTJmiO+64Qz/84Q/14IMP6uKLL9bvfvc7SV+d1Vm2bJnuueceTZ8+XcnJyVq9erUOHjyotWvXfqfJAQAADAimuLW1VZWVlcrLy/O3hYSEyOPxqKKiotNjKioqlJubG9CWkZHhDzI1NTXyer3yeDz+/S6XS2lpaaqoqNDMmTM79NnS0qKWlhb/48bGRkmSz+cLZjonrb3lH73Sb2/qrbXoTaxz32Cd0R1eH32DdQ7s0xhzyvv+pqDCzpEjR9TW1qa4uLiA9ri4OL3//vudHuP1ejut93q9/v3H27qq+baCggLdf//9HdoTExNPbiLfA65l/T2C7wfWuW+wzugOr4++0ZvrfOzYMblcrl7rP6iwc7rIy8sLOFvU3t6uTz75ROedd54cDscpfS6fz6fExETt379fUVFRp7Rvm7FuPcfa9Qzr1nOsXc+wbj13fO1qa2vlcDiUkJDQq88XVNiJiYlRaGio6uvrA9rr6+vldrs7Pcbtdndbf/zf+vp6xcfHB9SkpqZ22qfT6ZTT6Qxoi46ODmYqQYuKiuLF3AOsW8+xdj3DuvUca9czrFvPuVyuPlm7oC5QDgsL04QJE1RWVuZva29vV1lZmdLT0zs9Jj09PaBekjZu3OivT0pKktvtDqjx+Xzatm1bl30CAACcrKA/xsrNzdXcuXM1ceJETZo0ScuWLVNzc7Oys7MlSXPmzNH555+vgoICSdKtt96qK6+8Ur/5zW80depUlZSUaMeOHXr88cclSQ6HQwsXLtRDDz2kkSNHKikpSffee68SEhKUlZV16mYKAAC+l4IOOzNmzNDhw4eVn58vr9er1NRUlZaW+i8wrq2tVUjI1yeMLrvsMj3zzDO65557dPfdd2vkyJFau3atLrroIn/NnXfeqebmZt18881qaGjQ5MmTVVpaqvDw8FMwxe/G6XRq8eLFHT42Q/dYt55j7XqGdes51q5nWLee6+u1c5jevt8LAACgH/HbWAAAwGqEHQAAYDXCDgAAsBphBwAAWI2wcwKFhYUaNmyYwsPDlZaWpu3bt/f3kPrM5s2bNW3aNCUkJMjhcHT4YVZjjPLz8xUfH6+IiAh5PB7t2bMnoOaTTz7R7NmzFRUVpejoaN14441qamoKqHnrrbd0xRVXKDw8XImJiXrkkUd6e2q9qqCgQJdcconOPfdcxcbGKisrS7t37w6o+fzzz7VgwQKdd955Ouecc/Qv//IvHb58s7a2VlOnTlVkZKRiY2N1xx136MsvvwyoKS8v18UXXyyn06kRI0aouLi4t6fXq1asWKHk5GT/l7Slp6fr1Vdf9e9n3U7OkiVL/F/rcRxr17n77rtPDocjYBs9erR/P+vWtQMHDuinP/2pzjvvPEVERGjcuHHasWOHf/9p9R5h0KWSkhITFhZmVq5cad555x0zb948Ex0dberr6/t7aH1i/fr15v/9v/9nXnzxRSPJvPTSSwH7lyxZYlwul1m7dq3529/+Zv75n//ZJCUlmc8++8xfM2XKFJOSkmK2bt1q/vd//9eMGDHCzJo1y7+/sbHRxMXFmdmzZ5vq6mrz7LPPmoiICPOHP/yhr6Z5ymVkZJinn37aVFdXm6qqKvNP//RPZsiQIaapqclfc8stt5jExERTVlZmduzYYS699FJz2WWX+fd/+eWX5qKLLjIej8fs2rXLrF+/3sTExJi8vDx/zYcffmgiIyNNbm6ueffdd81vf/tbExoaakpLS/t0vqfSn//8Z7Nu3Trz97//3ezevdvcfffd5qyzzjLV1dXGGNbtZGzfvt0MGzbMJCcnm1tvvdXfztp1bvHixWbs2LGmrq7Ovx0+fNi/n3Xr3CeffGKGDh1qbrjhBrNt2zbz4Ycfmg0bNpi9e/f6a06n9wjCTjcmTZpkFixY4H/c1tZmEhISTEFBQT+Oqn98O+y0t7cbt9ttfvWrX/nbGhoajNPpNM8++6wxxph3333XSDL/93//56959dVXjcPhMAcOHDDGGPP73//eDBw40LS0tPhr7rrrLjNq1KhenlHfOXTokJFkNm3aZIz5ap3OOuss8/zzz/tr3nvvPSPJVFRUGGO+CpohISHG6/X6a1asWGGioqL8a3XnnXeasWPHBjzXjBkzTEZGRm9PqU8NHDjQPPnkk6zbSTh27JgZOXKk2bhxo7nyyiv9YYe169rixYtNSkpKp/tYt67dddddZvLkyV3uP93eI/gYqwutra2qrKyUx+Pxt4WEhMjj8aiioqIfR3Z6qKmpkdfrDVgfl8ultLQ0//pUVFQoOjpaEydO9Nd4PB6FhIRo27Zt/pof/ehHCgsL89dkZGRo9+7d+vTTT/toNr2rsbFRkjRo0CBJUmVlpb744ouAtRs9erSGDBkSsHbjxo3zf1mn9NW6+Hw+vfPOO/6ab/ZxvMaW12dbW5tKSkrU3Nys9PR01u0kLFiwQFOnTu0wP9aue3v27FFCQoKGDx+u2bNnq7a2VhLr1p0///nPmjhxoq6//nrFxsZq/PjxeuKJJ/z7T7f3CMJOF44cOaK2traAF7AkxcXFyev19tOoTh/H16C79fF6vYqNjQ3YP2DAAA0aNCigprM+vvkcZ7L29nYtXLhQl19+uf9bw71er8LCwjr8eO231+5E69JVjc/n02effdYb0+kTb7/9ts455xw5nU7dcssteumllzRmzBjW7QRKSkq0c+dO/0/1fBNr17W0tDQVFxertLRUK1asUE1Nja644godO3aMdevGhx9+qBUrVmjkyJHasGGD5s+fr//4j//QqlWrJJ1+7xFB/1wEgJO3YMECVVdXa8uWLf09lDPGqFGjVFVVpcbGRv33f/+35s6dq02bNvX3sE5r+/fv16233qqNGzeeFj+zcybJzMz0/52cnKy0tDQNHTpUzz33nCIiIvpxZKe39vZ2TZw4Ub/85S8lSePHj1d1dbWKioo0d+7cfh5dR5zZ6UJMTIxCQ0M7XHVfX18vt9vdT6M6fRxfg+7Wx+1269ChQwH7v/zyS33yyScBNZ318c3nOFPl5OTolVde0RtvvKELLrjA3+52u9Xa2qqGhoaA+m+v3YnWpauaqKioM/o/6bCwMI0YMUITJkxQQUGBUlJStHz5ctatG5WVlTp06JAuvvhiDRgwQAMGDNCmTZv02GOPacCAAYqLi2PtTlJ0dLQuvPBC7d27l9dcN+Lj4zVmzJiAth/+8If+jwBPt/cIwk4XwsLCNGHCBJWVlfnb2tvbVVZWpvT09H4c2ekhKSlJbrc7YH18Pp+2bdvmX5/09HQ1NDSosrLSX/P666+rvb1daWlp/prNmzfriy++8Nds3LhRo0aN0sCBA/toNqeWMUY5OTl66aWX9PrrryspKSlg/4QJE3TWWWcFrN3u3btVW1sbsHZvv/12wH8EGzduVFRUlP8/mPT09IA+jtfY9vpsb29XS0sL69aNa665Rm+//baqqqr828SJEzV79mz/36zdyWlqatIHH3yg+Ph4XnPduPzyyzt8pcbf//53DR06VNJp+B4R1OXM3zMlJSXG6XSa4uJi8+6775qbb77ZREdHB1x1b7Njx46ZXbt2mV27dhlJZunSpWbXrl3mo48+MsZ8dVthdHS0efnll81bb71lpk+f3ulthePHjzfbtm0zW7ZsMSNHjgy4rbChocHExcWZn/3sZ6a6utqUlJSYyMjIM/rW8/nz5xuXy2XKy8sDbmf9xz/+4a+55ZZbzJAhQ8zrr79uduzYYdLT0016erp///HbWa+77jpTVVVlSktLzeDBgzu9nfWOO+4w7733niksLDzjb2ddtGiR2bRpk6mpqTFvvfWWWbRokXE4HOa1114zxrBuwfjm3VjGsHZdue2220x5ebmpqakxf/3rX43H4zExMTHm0KFDxhjWrSvbt283AwYMMP/5n/9p9uzZY/70pz+ZyMhI88c//tFfczq9RxB2TuC3v/2tGTJkiAkLCzOTJk0yW7du7e8h9Zk33njDSOqwzZ071xjz1a2F9957r4mLizNOp9Ncc801Zvfu3QF9HD161MyaNcucc845JioqymRnZ5tjx44F1Pztb38zkydPNk6n05x//vlmyZIlfTXFXtHZmkkyTz/9tL/ms88+Mz//+c/NwIEDTWRkpPnxj39s6urqAvrZt2+fyczMNBERESYmJsbcdttt5osvvgioeeONN0xqaqoJCwszw4cPD3iOM9G//du/maFDh5qwsDAzePBgc8011/iDjjGsWzC+HXZYu87NmDHDxMfHm7CwMHP++eebGTNmBHxXDOvWtb/85S/moosuMk6n04wePdo8/vjjAftPp/cIhzHGnPx5IAAAgDML1+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLX/D7gF/+X+gpE6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented basic units?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs (units) out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "56fd28b5-544a-4253-bec3-2090f733f943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#special for CA - ID the problematic enveloping coastal tract\n",
    "#plotPoly(tractGeom[1470])\n",
    "#plotPoly(countyGeom[39])\n",
    "for t in countyTractList[39]:\n",
    "    if tractCP[t].x < -120.8:\n",
    "        plotPoly(tractGeom[t])\n",
    "        plotCenter(t,tractGeom[t],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b7b8549-ef15-4145-9881-f4e83d391ff0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "splitting a long border tract 5205 that surrounds several coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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sb0aJ0DgySpZxpGcoRcaSoSzYkmMqBQVX43Cto/HUdygqusHocMQi0tc2RMVy6QkSqTHUPkQsEMNkNZG3PA9F5vpkLUmGUkRV7SiKhXh80OhQ0kJVxZ/R0PBp4vEAFovX6HDEYqEpcsESs6brOkPNQRIjo9MZHCVOcspT38todluIBqLYZKgsY0gylCKKomAy5xKL9xsdSlpIxAMoqhOz2WN0KEKILBcPxQg2B1FUlZzKHCwuy7w+X06Zm4EjA5IMZRBJhlLIZPESiw0YHUZaiCcCKIqN0WWn8klepIZUmhazMdgYoPD0hPuFoCgKJquJcH8Ep09KjGQCmUCdQmazh5gMkwFQVHgDWtJPb+8fjA5FLCIFFTn0NAeNDkNkkEBLEO+ShR+qz12WSzwYY/Dk4II/t5g5SYZSSFXtJLWo0WGkBa/3Iqz2ZbR1PGZ0KCITTLPDx+mxEg3Lti9i+pLhBNaciesCzTfvEi/2PDs9+3pJxOT3Np1JMpRCZtVGMinJ0BklhVcRCOxB12VoQ6SO/D6JTGLPs1NQ7yNwIkDML9eHdCXJUAqZTQ4SyWGjw0gbefmXoyX7CYWOGR2KSGOaps1sWpmOfMoW0xLsGMZZZPycHVVV8a32ke+zGx2KuABJhlIoL+8SwkMHiUZ7jA4lLSinr3C6LhcucWG6pqMo08+GKlbl0dYwOH8BiUUjEYxhzzc+GRLpT5KhFCopvhVFMdHZ+XOjQ0kLp5q/D0A43GRoHCL9zSAXQlVVTGaVeDx7t74RQqSWJEMpZLF4cXk20Od/2ehQ0sLqlf8Pp6ueg4f+jqamb43N9Ugmw4yMdBocnUgXs5kBVL4il/aj/pTHIhaPoc70GCITmUHqDKVYLNZHYf4Wo8NIC07nEi679BccP/ElGk9+ke7eZ7h44/c4dOSf6e15nBXLP09FxR1GhynmUU/nAOGhkUnP0TUdh2tmxelUVUWVStRiEjF/lJzVPqPDEBlCkqEUS0S7cDgqjQ4jbSiKwvK6fyDXu54Dhz7KH/90BYnEIAANxz9HSclbMZvdxgYp5kXQP0w8mqBmedm8tO/02hgaiJAjc0LE64wMjmB2yuVNTJ8Mk6WQpsXQtDBWi2wk+XpFRTewYvmnSSQGsVjLuGjDD0GPc/LU140OTcyDZDJJd9sA5TVF8/Yc+SVOAr2ReWtfZKZENMFQ6xDeGtkTUUyfpM4pFE+MVsY1m+WPcCLlZe/B7VqOw1GFzVZETc3f0dT0X1SUvwens9ro8EQKnTrSzpJV5fP6HJ0nAxTXyN534iwtqdF/eICiDQu39YZYHKRnKIW05OjcCFWd300AM5WiKOTmXozNNtpbUFX5QUClpfUhQ+MSqdXV2kdBSS5ms2lenyeZ1LDY5POcOKv/YD8F6wpmVKpBCJBkKKXs9lIU1cHw8FGjQ8kIFosXa+INtLd/l/Zje4wOR6RIdCRObsEC9NhIIWpxjv7DfeQuz8VklsuamDn5rUkhRTHhcK3AH3jN6FAyRrhttJeoo/X/DI5EZBq7y0I4KNsbCPAf9+Msc2NxSK+8mB1JhlKsMG8zfv8ficb6jA4lI9hcoz0IDqeM8YuZKazKob8tZHQYwmDB5iGsHiuOXNnqQszejJKhBx54gPr6ejweDx6Phy1btvDkk0+OHd+6dSuKooy73XPPPZO2+frzz9y+9KUvze4VGayq6sOgmGg49nmjQ8kI+YXrAAgOnjA4EpFpFEWZ2Z5mYtEJdYdA0XEVu4wORWS4GSVDFRUV3H///ezevZtXX32VN73pTdx6660cOnRo7Jy77rqLzs7OsdsXv/jFSds899zOzk6+973voSgKt99+++xekcGs1nxW1P0zvT2/YWDgT0aHk/Zi7a0A5HiWGByJSBW700rTsQ6ajnVwdF/T/D6ZIhOHstVIIEo0EMNTJSsKxdzNaCnGLbfcMu77++67jwceeIBdu3axZs0aAJxOJyUlJdNu8/Xn/upXv+Lqq69m6dKlMwktrZSW3E5b+085eOQfueySX2G1St2hCynQywgA3qS8oS0WxeVnq/42HeuY1+fSJRfKWqH2YXxSYVqkyKznDCWTSR555BFCoRBbtpzdfuKhhx6ioKCAtWvXsm3bNsLh8LTb7O7u5oknnuAv/uIvJj0vGo0SDAbH3dKJoiisXf1FtGSEV/e+l3h80OiQ0lbhhjdiHS6nw/+/RocihMgQ8XAck31+SzeI7DLjZOjAgQO43W5sNhv33HMPjz32GKtXrwbgjjvu4Mc//jHPPPMM27Zt40c/+hHve9/7pt32D37wA3Jycnj7298+6Xnbt2/H6/WO3Sor02/7C6ezhos3PkQs2s2re95HPJ5eCVu6UBQVb/sbCeW8xlCnzBtabOa73osik4ayUuBUEO8SKW4rUkfR9Zl1NMdiMVpaWggEAjz66KM8+OCD7Ny5cywhOtfTTz/NNddcw4kTJ6itrZ2y7ZUrV3Ldddfxta99bdLzotEo0ejZJbXBYJDKykoCgQAeT3oNtwwNHeHVPXfgcq/k0k3S+/F64QN99P5kN41XfRRPbDP1V/8XVmd6/RuK2Ws+3kl1Xem8tK3rOm1H/FSulmHobBLui5CMJsgpzzE6lBlL9EUwF8heeucKBoN4vV7Dr98z7hmyWq0sW7aMTZs2sX37dtavX89Xv/rVCc/dvHkzACdOTP2J//nnn6ehoYEPf/jDU55rs9nGVrSduaWrnJxVrFr5OYYCLxOJtBkdTloyJZxUqvcQcDzPy0/fRjIRNzokkQGCvSO482e2273IfOHuUEYmQiK9zbnOkKZp43ppzrVv3z4ASkun/mT43e9+l02bNrF+/fq5hpR2CnxbQTHT1yeFBS+k9vKPUOv4NFFnE8MdDUaHI1JlHic4B/rD5BY75+8JRNoJNAdxl7uNDkMsQjNKhrZt28Zzzz1HU1MTBw4cYNu2bTz77LO8973vpbGxkc9//vPs3r2bpqYmHn/8ce68806uvPJK6uvrx9pYuXIljz322Lh2g8EgP/vZz6bVK5SJzOYccjyb6OzZYXQoaa2o7hoAhvuOGxyJSIXoSIxYdB57+fT5n5Mk0oeu6yRCMexSXFHMgxktre/p6eHOO++ks7MTr9dLfX09O3bs4LrrrqO1tZWnnnqKr3zlK4RCISorK7n99tv51Kc+Na6NhoYGAoHAuPseeeQRdF3nPe95z9xfUZoqLbqBYyf+jVhsQJbaX4A9vxw17uLowD8wsGMXq6/+LCarvPGlg/7uQQIDw5gtJpIJjdLqQuwO6wXPH+gNMNg3RN26qnmLSSZPZ5fBY4N4l+YZHYZYpGY8gTodpcsErMmMjHTwwovXoJOksPDNrFvzZflUy+gE6oGHjlD2L1tQHWZ6jjxDw6n/R8zWiT2yhKVVH6do5VZMVpl0aJR4LE7riW6Wrq4gFo1jtphoPdlNMp7EZDKhTzAWpmkaS1dWzGtcbUcHqFg5tw8W8YTGkcaBse9f/yepKAq6ro/7W/W4rFSVyZyVhaRpGv6j/oyvKyQTqM+XLtfvGfUMidmz28vYctnv6ep+nJMnv0xf8c0UFl5ndFhpp2jV1RStep7+4y9y+NgnOdz3N5z4QwWbr/oVVneu0eFlHS2pcfxgKyvX1wBgtY1uhFm9bH5WiM2EloLPcR09ISpL3OR5p98Dub+hjyokGVpIgycC5C7LNToMsYjJRq0LyOGoZEnNR/Dkbub4yf8yOpy05qvbwhtuepZ1Zd8l5mijcdfX0JIJo8PKKt1tA5w82sbydVWopvR7q9CTqenUlg7aDJDUMVmlyKKYP+n3DpcFCvK2EIt2Gh1G2lNVlaKVW3GF1tOhfp9nn17LC7+5iYGTu40ObdFrPNyK1WFm2ZoqzJb07ED2lbvpaZZipotduDeCTUooiHkmyZABTCYnmjZidBgZ49Kbf8ba4m9RZvogcdXP3qY/4/knriI27Dc6tEXLZDKR50vP+XdnuPPsRIZiRoch5lmkNyy70ot5J8mQARLJMKpJJtFNl2oyUbzmWla+6R/ZctXvyBvZSszRRsTfbnRowmCKKmNci1kinkQ1y2VKzD/5LTNAPD6AyZTen7rTldWVy9I1fwdAsPuwwdEsTq2NXeQWZkZhO1mRubgNHvPjXSZ7kIn5l56TARY5XYuDIpMBZyu3ej32/StoCT+Ir+9ynAXzu4Q7mwwFQqgmldz8zEjWdXQ0TSOZ1Dl80j9W8XoknuSiFT6sVnmLy1TB5iAOnwNVlc/sYv7Jb5kB7I5K4tEOdF0zOpSMVbv0Y8QsXby472pe/PXbSMZkDlYq9Hb4Ka8pMjqMaRvRdA4cG+DA8QHW1uaxfmUB61cWcMmaQo6cHDQ6PDFLoa5hUMFVInOFxMKQj00G8Hjq0bUIw8MN5OSsMjqcjFSy5jryqp6leff3aHV+kyPPfp61199ndFhppbfTz9BgiPKlRdhso9WiWxo7ScSSePLcFJTkjjt/sD84VkcoU4yoOutXFo67zx8Y4XhLgLXLzhZk7OgZZiAQRdPGL8f3D0XZtGr844WxIv0R4uEkuUtleEwsHEmGDOD1XASKhcHBlyQZmgNbTj7Lt/5/6M/otCnfJPJEE0tX/C2+ZZcZHZrhopE4oaEwS1aW09TQgWpSiEbilFYXkON10dft59TR0QnoZ+bd2J1WKpYWGxn2jE00Y6ilc5hL141/Hf3+KOtWZHb14mwQC0YJ90XwrZAti8TCkmTIACaTHau9iqHhI0aHsiisuPpeYjsG6HH8lH0tuyhqfAerrvp/mO3Z28Xe0tjJsjWVKIrCkpXljERi2OyWscSnoDiPguLM3uepuS1IUeH5/8YOu4nXjvZRW+lhKBSnb3CEzv4w65BkKJ1pCY3BU0GK1ktPnVh4MmfIAJoWJx5tw+1abnQoi8aa6+7j4mW/oiR5Bz2WR3n1qcW76e9kWho7aTrWgcNpG7fSyu6wpv3Kq0AoxmBoenWDEkkN/2CU4gLneceW14zOHTreEqCxLci65T6uv7wy1eGKFPMf81OwRhJWYQzpGTLA8PARdC2K17vR6FAWDVVV8VatxVu1luTvwvTZfkOotxlXYbXRoS2YRDyBltSpWV5mdCiz0tQbAgWiiSTF3snrcB07OciaKYZSLlpVyM/+0JjKEMU8iY8kUC2q1BQShpHfPAMEAntAsZCTs9roUBalFW/4Z9Skk6Mvf9boUBZUy4kuKmsza87PuVRFYX11Hq29YfY2DvBak5+DzYMcaB7kUMsgieTo6suTzb0MNHcxMjJ1L9I7rl0632GLFAg0ykaswljSM2QAs9kLeoKRkS6czuzpuVgotpx8Kt130xT7d9r3/JLyjW8zOqR5FwmNYLaYMJkyt37VmUG8i5f5iCc0zCZlbGgvqWnsbxrEH4wSTA5TUWTB5Zp6v6p0HxoUEO6PYHNb5N9KGEqSIQMUFd3E0WOfpa39YZbXbUPT4vj9L6AoZszmHNzuVahqZi1xTjdLLv9LOn77U45q/0DwqYMsv/L/w2S1Gx3WvGlt7GZ5fYYn1udcCy2vGy4xqSoXLc1H13VePnEKNaZLMb40oGk6kUCU6MAIiqajn65coCiga/p526XoryttgA6qVSV3ae7CBCzEBUgyZACTyU5Z6Ttoa38Yn+8KTjY9QHBw19jxpUs/zpKajxgYYeZTVZXVK7fT1PAgHbYfEv2/Xjbc9FWjw5oXTQ0dGT08NhOKMtpbZJO5JXOiazojgRiR/jAk9dPZyzmZDJyu5v26+15HVcDmsZJblYNqydxeSSEkGTLI0iV/hz+wh337PoBqcrNh/f/gdC7l1X0fIhJpBeDkqa/T3PIgNlspF63/Fg5HlcFRZxbfssvwLbuMxucfoEn9d5p3XUz1Ze83OqyU6ukYwOm243At3l6vcyWTCWw2C/GhqCHPPxxL8Ep7AN154bfOc/OG1+cXw8NRVp3b66soYz0oubW5M954Nh6KMdwROtsLo5/teTnny7P5zTnxWHNs5NZ4UM2SxAghyZBBLBYPl2x8iJaWBykquhmXqxYAl7OGfv8uAoE9NDU/gC//CoLDR3h597vYfPHPsdszc6WQkZa84S/x//YlTsX+g9LQLVhduUaHlBIdzb2YzCrF5dmzHFlndDQtnlzYrWw0TeOZVj8Oi8qfb10y6yG64+0B8srPr6yciCYZODqAxW4mZ4nngvNntKSG//jg6IiiAmaHGe8Sr6zCEmKO5C/IQCaTgyVL/nYsEQJYXvsJEnE/r+5+J6piYdXK+7h0009RUHjt4N+j60kDI85MqqqyctOnSVqHaN37v0aHkzLxWGJRJUK6PvU5ZpOZeDLBsqpSjpxonv+ggFhS4/HGXt5QlsvlZXnzMlfJbDPhW+3DWuRg4MgAgVMB9NM/kGQ8Sbg7zMCRAfwNfvJqveSvzCd/RT6eKo8kQkKkgPwVpRm3ewVbLtvBxZt+xhsufxar1YfNVsy6Nf/JcHAvTc3fNjrEjOQurcUdupgu/y+NDiVlzpuMmob6glF2HuhOaZuxhEZEi6PpEAgOp7Tt1wuMxPlDUz9vrS3EvgBzYuxuK77VPpxFTvzH/Aw0DDDUHASzQv6qfHyrfTI3R4h5IMlQGrLbSvB6N2Kx5I7dl5e3maqquzh58j/p6dmBpsWNCzBDFeRuZcR1gq6Dvzc6lNRI85XImq7T1DvMuppcDrcGxh1LaucPc3ldFjr8kSnbdVktlOblsaaummNNHSmL9/W6Q1Fe6Qry5trCBV+5ZnFZyF8x2vuTuywPp2/yIpRCiLmROUMZpHbpx/AH9nDg4F+jmjwUFd1AafEt5OZuRlXln3IqNZv/nJ4dv+VYy79QsOwNGb93maJAdCSOzZ6eZRhePTXAhspcrFYTOvBakx9VUUhqGiZFJZ7UKMq3U5E3uqVGVYGLvaf85DktOGwT/z6f6OzEHxkZ+76mopiTLR0srUrtXLqWYISW4AjX1iyeYUghxIXJFTSDqKqVSzY+wvDwEbq7f0NH96/p6vwZqsmNzVGDx70cp6MKi9mDxZJHQcHVmM05RoedNkxWO6sv+gKvHn0bp156kLqr/t7okOakuq6MY/ub07a+0PrKXI53DbGmMhdfjg1fzvlFEhu7hmjsGqK2JIfDLYOYVYWeYJTqwvPfmoYSSSKxOFetqBu7rzDfS2tnb0rjbhwM0xuJ8caKzN7IVggxfZIMZRhFUcjJWU1Ozmpqa+9laOgg/f07GQ41Ehg6Sl/fM2jJYXQ9jqq6qKz8ILVLP4qiyIgogLdiNd59l9OW/C7lfW/HWZDZG3jOdCn2QrJZTOhTLPqqLcmhvT/MgaZBqkpceC/Qy9XtH6Q9OIRlglVWqqKiaxpKCoayjvYPE0poXFaaO+e2hBCZQ5KhDKYoCh7POjyedecdi0a7aW75Ls3N38BuL6Gi/A4DIkxPay7/N1566a28/OqtKCjUFm7DZHHi8JSRW7PB6PBmxGJN7z/hkUSSWCKJdZJaNuU+J+W+83efP9dgKERdWTE5Fut5x4oL8+nq6ae0pHDWceq6zp/aB8m3W9hU7Jl1O0KIzJTe76Ri1my2YpbX/RPxeIDjJ+6nqPBGrNbJd/nOFo78Mi7e9ChHXv40QecLNAQ+OXqgV2FF33YqLn6nsQFOkz6dtegGq1+Sx4m2IKurcufUzvKyUva2tLKx5vwhwZICLzv37Mcf70e9QA9oJDZCTVEVee6zcTxxshev1QwKaBpcVJxDTponl0KI+SF/+Yvc8rp/pLvnCZpbHqRu2T8YHU7acBfVcMlbfoSmaYS6TgBwdM/nOKZ9GuuRAopWXW1whFNLJpNpvz+XVVUZic+9QKKiqljNJvqHhvHluMcd6z90ijJLmOWV6y/4+P5gL/Hk+BpdNlVleZ6Tomls+CqEWNzS+51UzJnFkkdlxQdobfsBQ0NHJjxH0xJ09/yW5pbvEI31LXCExlJVlZyy5eSULeei676DY2Q5h1r+jsHm/UaHNqWOph6KytK/t684105zX2jO7aytqOBEz/jJ0oNNHeRUFFHqm/znkNCSmNTxQ3XX1vhoG47ypzb/nGObrvTvyxPzKn2n+GU9SYayQE31h7HaSnj5lbdy+Mg/k0gMjTu+6+W3cPDg33LixJd44cVr6e39g0GRGstsc7Bx6/cxJ3zsPfo+Wl/9CeG+NqPDmpCmacSiSezO8+fQpJtyn5PBFO0ltqqkmN1NTTTub6T/cBMkdWy57ikvMloyjmmC8hMbiz3U5Tv5U/toQjTfQ49yLRQiPUkylAUsljwu3/w76pZto6v7cfa8dve4oo0jI20sqflbrrziJby5l7D/wD28uveD+P270DRjNsQ0ii0nn0uv+BmO2HKOBf+JF/dfxYnnv2F0WOdpO9lD1bISo8OYtnKfi5beufcOeVxOvJY8CpdV4ltdQ25tOTB1EqPpSVR14pVqRU4bhU4rz7X62dnm59WuwITnCSEWL0mGsoSqWqiq+hAXrX+QocBuGo7/69gxsyWPZDKMxZLHRfXfZs2arzASaWHP3vfy3B8vJxw+ZWDkC8/mKeSyWx5lU+0v8IQvpzXyAO17HkOboGqyUbSkhtWWnsUWJ1LgsTGQot6hSDyJ53U9YrqWmPQxSS05aXmJ5XkurqzMY2tlPsPx+dv/T4bJhEhPkgxlmby8zSxf/v/oaP8xx4//G13dvyaZCI4VZ1QUhZLiW3jDlj9wycWPYTZ72X/o4xmxcinVcqvXs+6N/4ElVsTRwf+PfU/+dfokRBk43uLz2mjrD8+pjVg8STw5/ndxuP8Urrwlkz5OR5tWIrK3J8gan3vqE2cpA//ZhMgKkgxloYry91JZeRet7f/LoUMfRVXt5Oe/cdw5imLC46ln5fJPExraT3v7Q1mZENlzi3jjW55mqe0f8Tv+QNeBJ4wOKWNV+lz4h2NzauNYZ5ANS8ZXhtYiI5jdk2+tMjySwDONbUtcZhMnA3NL2IQQmUeSoSykKArL6/6Rq696jSve+ApXvnEXXu9FE55bUHA1RcW30XDsX+jo+MkCR5o+qrf8BbZwDS0t/2N471Bv5wA53smLFC5WVrN6XjkBncmHtXRdR5mgcvVElue7KHLaONI3POsYJ6NpOgO9IcN/h4QQ40kylMUURcVqzZ/yQrFuzb+T57uak83fQp9qf4VFSlVVqkvuJuR6jVN/esCwOOLxBIGBEL7iXMNimAtN14knZ/875LKaaOwYP8E5EY/S3r6Hjs59dHefXz6ita+Jcl/VtJ9jiddBV3h+Fg7UVXhJui0caQvgD45M/QAhxIKQZEhMy9Lqe4iNtDAw8LzRoRim8uJ3kRe5ltbQd9GS8zfJdjJNDR0sXVVuyHOnwrpKL/tOzq6uT2A4SnAkQSg2/mfv8q3CQRVlpRvweEpobd097vjwSBSvc2Y9aXV5Ll7sSH39IVVRKHRYWVOVR9tAWHqIhEgTkgyJafF6N2F31tHS9r9Gh2KoiiXvImkNMNC4a96fKzw8QsO+JjqaexiJxEgmk1htlrSvOj0Z1aRSV5bD0faZL19v6RmmPN/BmsrccffbPU5ioQgADkce+flVtHfsmVOcxU4rseT8zpFbVZk7q5+DECL1MvddVSwoRVEoL70Nv/85ksnsqj10Lt/yKzDFvLSffHTen6uvc5DatZXkF+XSfqqbo3ubKCjJnffnnW+5LivRxMx7RCoK3TT2DBOf4LHnDvW6XIXk5y2jre1Vjp96Cbd95qvDXuwcZEupd8aPmwmzSaXU6+C1k/0kpYdICENJMiSmzW4rRdei6Hp86pMXKZPZQr5yDX52oiUnr20zUx3NvTQd6xgbOtE0DbPZhN1hpXZ1Jas3LcWV40jpcxpFncUi87wcG+sq8jjeGSQ0Mv5nb3c4iJzuHQJwODxUVFxMWUk9PX6dxo6ZbTOzItfFrs7gjGOcqTyPnTVVeZzqGpr6ZCHEvJFkSEyb3TE6V6Xx5FeMDcRgpUtuJWkN0Hv02ZS2G43EKKsppOG1Zno6+4mOjE86p7siKhPM9pWYzSrravLp6B9fzTqRSGAynf925nI4uHh5JUMznBBd7LZhN6v0j8ytFMB0mM0qWVi1Qoi0IsmQmLZc7yZql/4DbW3/Qyh00uhwDONbdjnmqI/OlsdT1mZXaz95hTlYrRZWbqjBarGxvH76K6AyjdVhIhSafQ/j6/PCWDSG1X7h3edH4jPvxbu01MvurvnvHQoMR3HZz983TQixcCQZEjNSWfl+UCz0D+w0OhTDqKqKz3QtftPT+E/tnXN7gf4hYtEYuT4PMNoDlFvgXlQ9Qa9XW+Di5Bz2KmsbPFsYMTESwzJFQUW7ZXbJhs9uoWeeltmf0TEYocw3edFIIcT8ko8jYkZMJic5novo7H6CyooPTLrf02JWe/Hf4H/xBfY2vhvP4c3ouobDVsva6z47o3bi8Tg9HX7q1i3eXqCJmFQVfQ47dZW47GNfB3sGseY56Q/00+Zvw6SYxp2roHC0O8iwbgVFwWpSuHSJb1rPs6nEy++b+rikRCVvGhWsZyoaS2CdYHhPCLGwJBkSM7ak+sPs3/+XHGn4F1at+Nyi7sG4EEd+GVuufZKjO+8joO0jbm0jaHqR8lNvI2/JxNW8J9LR3Ev18rJ5jDSNKaBrOoo689+fwdNDbLqu0x5sxaw6cdgcrK9ZP+H5Q0oDl1UVAvCnEzObTH19TQG/PdnLjTW+lJc1ONUzzPIyT0rbFOlLT+ok+iNTn7iAtJiGtVR6JiUZEjNWWHANq1b+G0eObsNuK2bpkr8xOiRDmG0O1l7/rwCE+9p4cf9VhP3NM0qGkjEdqy07/wyXlebwWpOfDUvzzzt2onuIYCiOx25m2TnJQoc/Qs9gBEcoQSKepKurhbplq7Hbp7/KrrbQxR+P99Lqj3B5rQ+TqmBSlNH/qwqqMrrs3WU1o55O1LZW5vNiZ4A3lOdN0frMKJDRdaPEzFiK0m8bnURfeiVnRsnOd2ExZ2Vlf8bQ0BGaW79LTfVfoqqpH0LIJMno6PwXk3Vmb3aqOft61c5wWs2U5Ds41Dp4XiHFkZEkG5fm81rT2SrQ+5v9eB0WNizJZ8QZJhyIEY/Hp5UInTskV+J1UOJ1EI4l6A6MkEhqJHWdpAZJTSep6Wi6ztBInKQGF9fkkWO3MDKL2kiT6esPk59rn/pEIcS8k2RIzFpZ2Ttoa/8hA/4/UeDbanQ4aULWSM9ESa6DwVCcoZE4OefMyUmc3r+sqsDJq439OK0mKgtc5LmsANgKHTz7XCPr189+iMlpNbOkcPKCjLqu89LJAcLxJKUFDo70D7PKN/MijhPpi8RY6ctNSVtCiLmR/lkxa273aqz2Kjq7UrfEPFM5CqpAV4gEW2f2QMmdWFnu4UhrgJ7TG5fuP+WnxDfaw5bntrG+Jo8cp2UsEQJQVAVLqZmAapqwzVRRFIXLan1cvaIQ/2CUnY19KdlPTNf1WddaEkKk3oySoQceeID6+no8Hg8ej4ctW7bw5JNPjh3funUriqKMu91zzz1TtnvkyBHe+ta34vV6cblcXHLJJbS0tMz81YgFpSgKZSW30tf7FLpuzMal6cJsc2CO5TMUPH/XdDG1S+sKaOoapmMgzEg8SYn37PCRxaRSOcHS82J7LkPxEC/0nuKPPafY2d3IYX/7hO3Ptaihoii8YVkB1S4bz7cNzq0xoLVnmIqC1PQwCSHmbkbJUEVFBffffz+7d+/m1Vdf5U1vehO33norhw4dGjvnrrvuorOzc+z2xS9+cdI2GxsbeeMb38jKlSt59tln2b9/P5/+9Kex22UsPRPk5V6KpoUYHm4wOhTD5SqXM6A/O6NtOnJynfT3DM5fUBnkkjofiaTOJXVTL3sfXYUGlxTUcHnhEt5YtISrimvJtXv5XccRAvH5qQ10+ZICDnXMvRDjUDQhhRaFSCMz+mu85ZZbxn1/33338cADD7Br1y7WrFkDgNPppKSkZNpt/vM//zM333zzuKSptrZ2JmEJA+XmbsJkyedY45fYuP5BFGV+hy3SWfnSd9LX9mu6D/2B0vqbpvUYX3Eupxo68BXlzm9wGUBRFKoKp7fENxyOY50gmShzuCm1r+TFvlMkNJ0VniKKHTkpi9HrtFBkt3Cwb4i1BbNr91CLn+oCWcosRDqZ9ZyhZDLJI488QigUYsuWLWP3P/TQQxQUFLB27Vq2bdtGOBy+YBuapvHEE0+wfPlybrjhBoqKiti8eTO//OUvJ33uaDRKMBgcdxPGUFUba1d9kcGBP3Ls+P1Gh2Oo/GVbsIdrOd72OeJD0/udTCSSaEnZsXymmgcjVOdNvIpMURQuL1zKlcW1tIcHOTTYldLnznFZCEZmt0lva88wVYUu3E7r1CcLIRbMjJOhAwcO4Ha7sdls3HPPPTz22GOsXr0agDvuuIMf//jHPPPMM2zbto0f/ehHvO9977tgWz09PQwPD3P//fdz44038vvf/57bbruNt7/97ezceeHtHrZv347X6x27VVZWzvRliBQqKLiaurpP0db2PQ4f/RTJ5IUT4MVMVVVW1n+GuL2Hll/sQNemnqhiNptwuKy0nexegAgXj2RSx2qeuhdyo6+SpK7REh5I2XPbHGYur5xdvaEEOjapKyRE2pnxoPWKFSvYt28fgUCARx99lA984APs3LmT1atXc/fdd4+dt27dOkpLS7nmmmtobGyccOjrzKqMW2+9lY997GMAbNiwgRdeeIFvfvObXHXVVRPGsG3bNj7+8Y+PfR8MBiUhMlhV5QdQgOONXyAy0p61Q2bWvNGLpNYRpufhgxS8bQUm9+S9AGXVRTQd6yCRSGKexgVezEx9XhntwRBdoZFx23jM1lzmYltUhZFEMmsLbQqRrmb8EcVqtbJs2TI2bdrE9u3bWb9+PV/96lcnPHfz5s0AnDhxYsLjBQUFmM3msZ6lM1atWjXpajKbzTa2ou3MTRivsvID1K/7bwYH/sjxE1+ks/PnDA0dNjosQ3ivqyZ6PED7l17G/+sThF/rRU9e+DJaVJ5LT2vqei/EeDdV13HEH6ElOPdqu3NJhgJDMTwu25xjEEKk1pw/nmiaRjQ68cqNffv2AVBaWjrhcavVyiWXXEJDw/iVSMeOHaO6unquoQkDFPi2smzZP3LixL8BYHcu5w2XPTnFoxYf+/J8fPeuZOB3jQQPdmP6Uyd6npmCW5bjWH3+aqnj+9vxys7l06LrOrPZDu/qijx29wTxRxOsL5zd5OffnOolGJ99GQlNGX3PlC04hEgvM0qGtm3bxk033URVVRVDQ0M8/PDDPPvss+zYsYPGxkYefvhhbr75Znw+H/v37+djH/sYV155JfX19WNtrFy5ku3bt3PbbbcBcO+99/Kud72LK6+8kquvvprf/e53/PrXv+bZZ59N6QsVC6eq8kMoisrQ0GG6un5BJNKCw3F2V3ZNi9HW/hBezwYsLN7hTZPbSuE7VgEQax+m9/EG+n94mKK/2YC1YvzFuLgyj/DQCLFYHKs1u7c2mUoioWEyzy6Z2FTk4VQgwrNtA2ytOH9PtKl4rGbesqRwVs8NsKLMy8nuYZaVSm+2EOlkRu8oPT093HnnnaxYsYJrrrmGV155hR07dnDddddhtVp56qmnuP7661m5ciWf+MQnuP322/n1r389ro2GhgYCgcDY97fddhvf/OY3+eIXv8i6det48MEH+fnPf84b3/jG1LxCseAURaGq8s+prLgTUOkf+OO4411dv+T48X/l1d3v4MXezZy6/J8IDyzuIpvWcjdld41u4BprHz7veElFAYqiSCI0DYPRBB7r7OdWLfE6uKgwhydO9RFNLGyxUKvVRFJWDwqRdmbUM/Td7373gscqKysnXQF2hj5BKdgPfehDfOhDH5pJKCIDWCxebM5aWtsewpOzFlBQFJVQuBEUMzVV99DU/HVi7g5e2/eX+I5eRfXGD+PIm36dqoxiUtCsEOs8PxkS0xM42UlLWx+r1i+dUztem4WbqvN5qs3POp+b0gWcx6PMZoxPCDGvZOBazBubrQyPaynh0FFeefU2Xnn1bbz8yltpaXkQk8lNbe3HuPKKPazM/RIaI7Qr/8Nrf/yI0WHPG0VRcF9aSujlLvTXzTvpbOnDV5JrTGAZQNM0ul46gtmqklPhwZEz9U71U1FVleurfHSEojzdNkBwZPLaQZ2hEQajs6svNO55QXqHhEgzsr5TzBtVNVNR/n5yvReTl7cF0NB1DdCxWkfnXVgsXso3vp1y3s7Rp/+Nduf3aHrx+9Rs+aCRoc8b5wof4T92kugfwVJydsL0SDhKaVWBgZGlr8EjTYwMj1C4aTkmswkaT6GkcALypiLP6O703QFCcY1LizzkTLD0/fhghDfXTL1VyFTKfU7aB8JUFcreZEKkC0mGxLyy20vJz98y9YlA3ZX3EvjdPhqVzxN4cg/LLv4ErsLFtarQWpmD7lbp/vFBiu9ch6XIaXRIaSvU3sNQ+wDeZeXkrkrdlhoTURSFy0py0XWdnR2DVLhsLMsd/29T4LDQFByhNndu/2YOu4Xe9gAVPqesKhMiTchfoph3E80Tm4jJbGHzW35Klelv6Tf/gV37r+H5J67iT7+5ke5DT81zlAtDtZspev869FCcri/vpvW+F+j+6SEsFhPRSMzo8NJG/8EmElGNkktX4sif30ToXIqisLU8j1A8yYG+oXHHesOxOSdCZ9RX57Pn5ADR2NyH3YQQcyfJkJhXdnsZIyNtM3pM3VUf5Y2Xv8hS+yfxmDagAIc6/oaWlx+enyAXmK3aQ9nHL8V9WSmK3UR8zwB85zidr7UbHVpaCLb0YHFY8S41biL9+sIceiPxcYl8KJG6eT4Ws4rLZsYyyxIBQojUkr9EMa9U1YqmxdC0iQtzXojVlcuSN9zF+hu/yqXXPYYndhnHhz9NwzNfmKdIF5Ypx0re25ZR8YnNFP7VegDMv2xDS8EE3UwW6uwjNjiMp7bM6FC4uCiHl7rObrhb5rLRm8LeuyXFbpq6ZWWhEOlAkiEx71yuWiKR1lk/3mxzsPGm71GSfC9t+rdp3/vL1AWXBkw5Z/cuS/SPGBiJsYZOdjAyEKKgfm7L5lPFY7MwfE4dovUFbg71py55sVvNROdQzVoIkTqSDIkFMddNW1VVZdU1n8EeXsqpjq+iJRdPD8rIcT8ARR/ZgLUsO1cYDR5pJqmBb016TZi/qMDNs20Dp7cAURhKcZHGquIcTnUGpz5RCDGvJBkSC8JuL2do+Oic2lBVlerSe4g6Wgi0HEhRZMZzrBzdFqLnG/vQUzgvJVP07TuGKcdF7rJpDo1Nc0J+KvgcVlbmu/hTxyA7mvvxp3gY02UzE5eaQ0IYTpbWi3mnaQlGRtpRUpB7h4eaUJJWPOWrUhBZelDO2VpCC8UxebNjV3Nd1+l9+QjupeU4C73Teszg0DAu+8L+fEqcNkqco8/5bNvAgj63EGJhSDIk5l0o1IDbvXpsG4Jdx3ZTXVhBaV7xjNsymezoyThaMsHcBt7Sh+owg9METlNWJULdLxwgr74O2wyqSXd1dbOyrnYeI5vcbDZ3PVfXQJjhSByAM/1bIwmN0EgCl13ejoUwivz1iXlnMjnH7cfkdbpJJGMcaD7EqooVmE1T/xr2n9gFQI5vFXTpBNv246u7fN5iXkhaLAnh5LiK1Itd5wuHKdy4EovDOvXJi8hQJE5d+fR6wYQQC0eSITHvVNVGONxEKJbkQHuE+spSCjw+yvM19px8jQ1L1k2aEAXaDrGv5b3j7lNMi+gimtDQVYifDJIYHMGcazc6onnV9dIRCjcsnXEilIzFUKeROAshxEzJBGox7+z2MpzOGgpza1lSvIQCz+j+TqqqsnHpeg40H5708T2Nvwcgf+RaAIrj7yC3esO8xryQVKeFko9uQneo9P28YdoVuzPFcFsPvXuO07v3BN2vNOCtLcfimvlGqwePNbK0pnIeIhRCZDv5mCUWlPK671VVpaaoklPdzSwpnnhZdU7BWuiGquV/zjLnJ8gpWz7/gS4wS5GT/Lcsw/+zYyQHRjD75r4ru9G0ZJLevSewetwUbqybc3vOgR5OduexvHTmlakDI1Ha+vpYVVYq+4EJIc4jyZBYcGdqtpyR586jY6D7gucXLHsjdJoY7NxL7RV/tRAhGsJel4uuQu8vGij+wDpUa2ZPEe/802FKtqzCZJn724y/8QRly+o4EZ9+BWgtmaShvZ2IruM2m1laVMCe5hZURUFVFTRNx+dyUV1YMOf4hBCZTZIhsaDKch20D0aoyBu/4aUvx0NPYIAi7/mrdcw2B+a4hyg9CxWmIUweG4UfXEvvDw/R9Z19lNy1IaMTImuOIyWJEMBITzd5W96A3tIyrfOPdHQQicdZUV6Oy3w2houX1Iw7r90/yO5TTbhsNlaWlaYk1km9vmtUCJEWpL9YLCirWWVkgi0ISvLK6AtOluyowOIvTmdfnkfRh9eR7IzQ9c29hF7pIt4VMjqsGYtHRlCtqUmEBo4fw1szukVH/3CYfS0t7G1uZm9zM50D4+v+xJMaLxxvpDgnh43V1eMSoYmU5+WyaUkNRR4PLxw/QWIRVTYXQkyf9AyJBedxWBgMx8h1nr+aSNO0Ced06EoCJUt+XW01Xoo+tJaeHx/G//PjAOS9ow7Xxcbt4j6VUNcA/mPt2HLsoCjEw1FKLktNYcxofx/5daPzxK5ZvXLcsca+fl5uPMnFS2roHgrR0tvDZUtrUE0z61HLd7vYXLuUl082UVdUQIHHk5LYhUh70lsJSDIkDFCUY+dUX+i8ZGhp8RIOthyhvmYNAJoWY2Cog1x3BZopgtmSY0S4hrAtzaXiU1tIBqL4/9CE/9HjaNEkOW8oNzq0CYU7+8lfVoqzLLXzbwaONZC75MIbt9YW+CjJcbO/rQ2P1cbmZbMvyGhSVbYsW8rR9g5aBvxsqKyYcVIlRMZZXItXZ02SIWGIiT6M2K02qgsrONx6FEVRUBUrXlcRB5sOoptimM3ZU5QQQFEVzHl2Ct65ggG7mcCvT2LOteFYk4YTfhUl5YkQQHSgn/zlKyY9x2WzsaGqKmXPubK8jHgyyb7WNgC8Dge1xUUpa18IkX4kGRKGuNCHEa/Li9c1vkJvSe4Gdh4txh95iSXcNf/BpRlFUch/Sy1dvSH6Hm2gtMyNOS/NCjNqqf942d9wlLwlxmy9YTGZ2FgzWuphYHiYPU3NAJR4cijLP3+Sv67r6ICqTD7mICMSQqQnSYZERiiyvYUO/odQbzOuwonrES1miqpQ9M5VdHz5Fbr+Zz/lf38xisn49Q8DHcMMD0ZRBlM/yTvu92NfsXLqE+dZvttNvtsNQPuAnz1NzSgKY+UhdF1HQUHT9bH7k5rGyqJiXM7X1YuSIQkh0pIkQ8IQhTk2uoMjFHum18NRe9lf0/3Hn3HopU9x6Vt+NM/RpSeTx0rRB9bS+639tP/nK5TefREmjzHbkvR3DDM8MIK3yEnVah/9SoRIVx+OktQMlYW7u3EUFqakrVQqz8+jPD9vyvN0XedQeyfhri7WlpXhtGfHBrxCZCrjP1qKrOS2mRkamf4yZqs7l6V5n2DI+QLte345f4GlOdsSL4V/vR49nKTzgT0MPd9GYmBkQZ47Go7TcriflkP9mMwK1WsLyC0arRflW1WBv6kPLTr9ooiTCba1klOVuT2AiqKwtqKMS5bUcLKvjz3NzRxsbaXdPzD1g4VYSDJ2C0gyJAxUlmundSA87fMrLrkDV/giTnTfR2x4cP4CS3O2Kg/Ff74O/HECT5yi899fIdEfmdfnjIbjtB8bpGq1j6o1PnKLzp/MXnrpCrpePZ6S5/OtWIG/4WhK2jLSaFJUzsbqapaWlrBCP7/GlhDCeJIMCcM4rWYUBULR6fUQqarK2ku+SNIUYt+zf0MyHp3nCNOXtTKH0n/aTOFf1oPdROfX9xDvmX5ieUZvy9CU5+i6TutRP0s3TD5spagK+Wtq6HvtxIzjeD2LO4dEZOavJ505zRasFovRYQgxnsxjAyQZEgaryHPSMTj9Xg138VJWFNzHkP1l9uz4MFoWVww2eazYlngp++jFKHYT3b+YWU9KsD/CyHCMU6/1XfAcTdM4+mIX1Wt902rTnuvC4nEx1NQ1o1jO6H6lgd69J+jdexz/4OLrv9d1ufIIkY4kGRKGK8qx0xWY/ryX8o23sSJvO0HHixx5+rPzGFlmMHms5NQXo/VGZnSx7W0ZonK1j8IqN00H+mg53M/J13oJ9J7tkWna30fdxYVYZrBHmndJKaGO/hm9hjNMVjOFFy2j8KI6nKUFiy55UE0mkvHsTeCFSFeSDAnDeZ0WgiPxGV34KjbdTnHinXSZHiYavHDPRrawlLhQQxra0PQmMPe2BCmqHt1ywp1np2ZdAVWrfSxdX0giptFyaHSidEF5DuYZ7jEWC41gznFOfeI5wj1+gic7xt2XX1ZEf9vsepjSlbuikKGWxb3hsBCZSJIhkRZqC9009s6sVk1B+dUAxIZn1wuxmFjKR+vgDDx6HH2SAoj97UO0HO4nNBijr23i+UK+cjdVa0YnSnsKHROeM5nBoy3kr5nZSrChlh6U1+1w7853Ew7O78TwhWZx2knG40aHIYR4HakzJNKCSVVQFUgkNczTLCaYjI0O55js7vkMLSNYipyY6/OJ7h8gvLcHyxofOmCzj/6JB/oiDHSEyCsZrQs0r3Qm3Gx30ockNXIqi8ipXPzbXiiLa+RPZLrFNzVvViQZEmljSYGLpv4wSwqmtweZ1ZkHgzDi78CZn54bmC6kkjvW0NL9Et1/asPmsaGqCsP+ETwFdiJDcZbUz/+eZiOdPQz3DmM62IRvbc3Y/ZqmkYwmsDjGF4kMnOwgOjiMq/T8LS6ARbnSRdM0htp6iA2f7fWyuZw4yvKIDYYJ9/rHX5/MKvnLKhc8TiGyiSRDIm0oU+zr9Hr5Sy+FNhP+jlfJr71knqLKLN7VxQReaKOs1ouiKoCXQG94bH7QfLOXFrG0tIjePePrDXW/dASbx0EyerrOjqIAOvbCXIo2Lr9ge2abhZHQCAx3YS+umb/AF1D+qmqig8PkVIz2gum6TjQ4TOBYO+YcO76V44cYY0MRuvcdp3hDnRHhCpEVJBkSaSUcSzAST2K3TL16yWR1YI75CCdbFiCyzGCt8aA+o5MMRMc2c/UWzmwyc0qck9cOHm/Du6QUZ8kFen8mUVJbTsuBRqxDB7F2nSJ/zRWo5sx+21JVFUf+2eRUURTs3hzs3pwJz7fmOMhbUo6/sY282oqFClNki0XY+zobMoFapJU1ZV56h6I09U1vMrUlWUQk3jrPUWUOi290wnOiz9iJxxa3k0hfgEhfgEQkOqtECEYTB5PVjOqtwFe3gcEjLzDcdizF0aY/q9eJxWGj69UGkglZmi9EqkkyJNJOZb4Tj8NC79DUFaYdpnKiLK7l13Nh8o5uCGp0MpRbV47/SDOBU50U1NfOqa3KVUvwn2xEceaRv+5KTBYb/QeeS1GkmcNdVkjRRXX07W9k8EQHWkK29hAiVSQZEmkp32VlMBxDm2SZOIDDXkHC0o2WlAsDQOTQaM0l6wLNEZqMp6aYkktWpqStSu8IaBoAjuJqvMs20bfv6ZS0nUlUk0rxxhU4y/PwN7bRf6QJ/4k2o8MSmUxWkwGSDIk0Nlp7aHjSc/JLLkOzhOlr2LlAUaW35HAcHTC5jd8Dy11ZnLK2dKsbRgbHvjc7XHiXX0zv3qfQTydJ2cTqcOBbUY1vVQ1Wj4uBBpk3J8RcSDIk0paqKuQ6rQyELlxVuWDFVZhHCmk++aMFjCx9uTYWodtV/H9oMjqUFDNBYvzvgcXpwbfmjfTufQotPr3K24uRqygPR1EuA0eajQ5FiIwlyZBIa4U5NgZCF547pJpM5JuuJKweXsCo0pfqtJB7ZSWRPT0kp7k1R0aw50Lk/ErjqtVO4UXXMnD4Tww2vIT/yC78R14kMZRdVckdeR4Uk0Iiuoj+zYVYQJIMibRX4LZNOpk617eJhL2PcJ/MnQBwbykDVWH4pU6jQ0kZPacIhibe00tRVQrWX03uis3krbqMvFVbCDQdWuAIjeddVsHgiXajwxAiI0kyJNJertNKIHLh/Zx81VsAGGjetVAhpTXVYca2Ko/AnsWTDKED+vSXlFu9xcQGOqY+cRGZ6RYoQgBSZ+g0+esRGaHIY6MzMPFycWdhFaZoPv6+3QscVfrK2VCCOhAndoHNWDOOAljdEAlM6/ScqhUMtZ+Y35jSkM3jIuIPGh2GEBlHkiGRETx2C9G4xkh84iX09sRyhmIyb+gM+8p8dLtKz9f30fpPz9P6ry8Q7w0bHdas6ckEVF0Gba9e8JzwH35K4uTZ4TG7r4yRnuxaZZVTWUSoI7vmS4m5UewmtIgU8pRkSGSMmgIXLQMTX9AVVBT5dR6jqAr5b6tDL7TivaYawkmG/9SBfoFkMt0piun0fmYX5rjmHURf+xPRl3YA4CpbRqg7G1dYybiHmD6T20oydOFpCNlCrh4io5R67ecNl2maRlRtwqrO/67smcS1oYjKT2zGc00V1o0FDL/USfePDqJPUcgyLZktaLEIlKyF9r0TnqKoKq7b7ibp7yP68u8BcBRWEu5sXMhIDecqLSDY2m10GEJkFEmGREbJsVsIRRPo+tkLevueR4k7OqioeZeBkaW34nesouD9q4kfCzLwi2MZlxDlLF1O8EQD5BRDePJhIOeN7yXedIxkZxPOkhpGBrJruxZHvodYMHOHRIUwgiRDIuNU+1w09599s2/u+hb20CqK11xrYFTpz7Hah/fmJURe7aHvp0dJDmdOTRpFUTBZzIRaToLTB8ELr5RLNB3BXFZN5NnHRh+bhauszE4roe4Bo8MQGUKBcR8ws9GM3iUeeOAB6uvr8Xg8eDwetmzZwpNPPjl2fOvWrSiKMu52zz33TNrmBz/4wfMec+ONN87u1YisYDGpWM3qObWHVEZcRwi0HDQ0rkzgubIC71trGTnST/t/vMLwy50Z8yboXrYGs9fHYNBE9MizFzwvOdCF/Y23YK2/DC0ymjRn25YduUvKSYxE6T3QiJZlr13MnCnXRjIw9cbYi9mMkqGKigruv/9+du/ezauvvsqb3vQmbr31Vg4dOruC46677qKzs3Ps9sUvfnHKdm+88cZxj/nf//3fmb8SkVXKch0kNI2e4Ajl+e8BYM+R9xIN9hocWfrLubyMso9fgmN5HoO/OEHfz46iJ9L/gqkoCjavl9xV9cTjQPD84S9d00g0HwfAsmoz8SOv4C5fznDrkQWO1nje6lLyV1XTt7+RcO+g0eGINKaYVfREZnwomi8zSoZuueUWbr75Zurq6li+fDn33XcfbrebXbvOFrtzOp2UlJSM3TyeqXfPttls4x6Tl5c381cisk6p10FC0/Gufx+b1/wBzRShZe/DRoeVEUxeG4XvWU3u7XWM7Ouj4xu7ibVPviluOtGK66Fjz3n3K6qKuapu7GvQsHh8JMLZWXvHZDZTtKGO2HCYQdndXkxG0zPiQ9F8mfVgejKZ5JFHHiEUCrFly5ax+x966CEKCgpYu3Yt27ZtIxyeeiLfs88+S1FREStWrOCv/uqv6O+ffIJkNBolGAyOu4ns5DNF6Dn2IrHhflCTDIeyrwdgLtyXlFD8VxvQIkm6H9iXMQmRFhmGsoug+cXzjilm89jX5ppVRJ76CfHhYFYPF+UuKcPmy6Fn3/GMGRYVC8tS5CTRN3Fh22ww42TowIEDuN1ubDYb99xzD4899hirV68G4I477uDHP/4xzzzzDNu2beNHP/oR73vf+yZt78Ybb+SHP/wh//d//8cXvvAFdu7cyU033UQyeeF6KNu3b8fr9Y7dKisrZ/oyxCKxb+fdtPs/xN7mdwMwYP+DwRFlHmtlDuUfvwRyLfT+qsHocKYUC/hBNYOnFMw26HtdpelzLvam/BJsl1yPs6uD7ueeJRqcXgXrxciR5yV/RTUDx7Kx9pKYDsWiZtxK01RR9Bl+TIjFYrS0tBAIBHj00Ud58MEH2blz51hCdK6nn36aa665hhMnTlBbWzut9k+ePEltbS1PPfUU11xzzYTnRKNRotGzk72CwSCVlZUEAoFpDcuJxaNt989pCPwDBdGbKVv6Z9hziskpW25YPENDR3j5lbdwycWP4fHUGxbHbIRe6cL/8+OUfWYLqt089QMMMnh4L7mrLzp7x4mnobQeXKN1pqK7n8a26U0TPrb/tX1o8fjo5q4bNy1EuGmn//ApvLUVmG0Wo0MRaUZPasR7Iph9dlSraUGeMxgM4vV6Db9+z7hnyGq1smzZMjZt2sT27dtZv349X/3qVyc8d/PmzQCcODH9PYKWLl1KQUHBpI+x2WxjK9rO3ER2qth0O77oTfSbn8LuLjI0Ecp0plwbALH2YWLtw2k7nHLeUvllb4KmP0IySeSZn2Op23DBx/rWb6Dw4kvQEtm7/UDeymoGDp8i0ifTC8R4iknFWuoiGYyRHMqc0hupMOcCHJqmjeulOde+ffsAKC0tnXZ7bW1t9Pf3z+gxIrutvurfMMfzOLD370kmpKz8bJm8o8lQ33cO0PO1vfh/exJd09MuMVLUCXqtVr4FGp7AXL4EbXjqoTCr08lIls41VFWVoouWEx0O0bu/EW2SKQkiO1kKHOjRZFZNqJ5RMrRt2zaee+45mpqaOHDgANu2bePZZ5/lve99L42NjXz+859n9+7dNDU18fjjj3PnnXdy5ZVXUl9/drhg5cqVPPbYaDG04eFh7r33Xnbt2kVTUxP/93//x6233sqyZcu44YYbUvtKxaJldXpYVfsFIo4THNv5BaPDyVjmQgd5t9eR/96VONYVEH6+g7ZP/5Ger+0l+Mzohqe6rpMMGluPZMItykxmqLociymA1ttGvGH3pG3kVJQxtHfn/ASYIXJrSvGtrqH/YBNDXX1GhyPSjLnAQWJgxOgwFsyMJgb09PRw55130tnZidfrpb6+nh07dnDdddfR2trKU089xVe+8hVCoRCVlZXcfvvtfOpTnxrXRkNDA4HA6Cc3k8nE/v37+cEPfsDg4CBlZWVcf/31fP7zn8dms6XuVYpFr3DFFZS03EGH8kOKTlyLb9llRoeUcRRFwXVJCQCOtQWMHOon1j5MtG2Iod+3YK/xolhN9Hx9H461PnzvO3+e4ILR9fOzIncBhEuxOEaINncx2YyYZPNhrGVlRAe6sOWXzGuo6Uw1myhcX8twex99R05RsGqJ0SGJNKJYTWgjibSeQ5gqM3qF3/3udy94rLKykp07p/6kdW53u8PhYMeOHTMJQYgLWrn1nxnc8SKHj93LZeVPYnG4jQ4pYymKgmNtAY61Bei6Tuu/vkD4QB+5t4wuhIgc7GfoT+3kvKF8wWPTNe3CO9gXrURpfhH8LVM0ouOt20T/a89icuRgdrhSH2gGcZcXYM/Pof9IE4qioDM6mdZTXYzV7TQ6PGEQc66NeFcIpdiEcqG/uUUi+zbtEYuWyWJjbf1/Erf0cnjnp40OZ9FQFAX3phKGd3US3tND6adGF0YEfn2Snh8fWvCluMmRKWqhVG8hPhwl3t1z4XNOv7Hb8kuID01e1yxbmB02fKtqyF9ZjW9lNQVrlhA41Uk0EDI6NGEgc5GTRPfi3/hXkiGxqHir1lJp/Sv6rI/TdeDJqR8gpiXvhqVY1+Thf/QYw3u6yb1lKQCxgwN0//AgicGFm1ug2uyTHteC/djWbGSkoYFE78Tbs+inJw3Hh/04iqpSHuNiUbiulsCpDhKx7FpZJM5SVAVTvn3RF2SUZEgsOrVv/Btc4Xoa2j8te5WliGJSKHr3apyXlTD02ybCh0Z7U3KuriTeHKTzG3tIDsXQdX3eV54pyoXftvRkkviRXVjXvIGcK68gcuQIiddVtI/ufnrS5fdivML1yxg4LIUas5lqNS36lWWSDIlFRzWZWLf5K2hKlAPP32t0OIuGYlbJu3UZji0lxE6OLoLwXF9N6UcvhiR0/NtLtH3mBdr//WUS/aOfIhP9EUaO+9GTqXsj1eLje6GSXc3E9j9H9LWdxA+/gHXjdWPHcq68kshrr419Hz95CHNFLWpOLv0HnseWl72Tp6dLURQ8VSX4T7YbHYowiBZNoixQEUajSDIkFiVXYTW1ef9IwPE8LS/92OhwFg1FUch/yzKUahd571iOoiiYvTZK7tmA4rOieC3o0SSd39hD748O0/WlV+n77kECv09dz4ISV+h86H9ItJ8k2ddOcqALa/2V2NZfhXXdFSgW67jzLRUVxNpGNynVBvswFVePtmMy4yyR1VPTYc/PQUlCuD97tzPJVrqmk/SPYM6ffHg600kyJBatqkvfizdyJY2DX2C4p8nocBYNxaRQ/lcbcV1cPHafpchJxf+3mYqPX0rZ31+MqchB9FA/jg2FAAzvbCPhT828Iu9Fmyi47T30HzhOsrsZ6+rNk55vX76c6KlTp2M/+5Zn85UT7ZHhn+nKrSsnHoowcLSZvkOnCLR2GR2SmGe6phPvCmEuXvwrCiUZEovauiu+hKrZOPjyR6XS7gIx5Vgp/cuLKPvMFnzvXkn+u1cAMPDTY8R7UrMqxeK0Y69biz+cTzI6ddVxRVFItB1H9RaM3ecqriLU15GSeLKFt6qE/NMrzcwWK30HT2btxp7ZIN4VwlLiWvTL6kGSIbHI2TwFrKj4PCHnARr/+HWjw8kaiqKMFWqzLctFtyrETgXo+sbelD2Ht7acwk3L6X6lYcpzTU4zye42zDWrXhcnxMPZuS3HXLlK8vHWVdB36CRDLZOUMRAZKd4XwVLkRFEXfyIEkgyJLFCy7iYKYrfSGnuAweb9RoeTdUxuK+Wf2kLO1gqUqIaeTF1PQjwQwuKculq946LNKKbzJ4DmrdqC/9ALKYsn21hsVgrX1WJy2ujZd5xYcPHXo8kGWjiOYlZRzNmTImTPKxVZbc3Wz2OJF3Lw4MdJxrJnv510oVpN2JblAhBrTV1PjP94O4Ub66Y+cZJufqvXhx6XOjpz4SzwUrShjnCfn76DpxjulEKWmUqPJ0mG4phzs2tLLEmGRFYw212sXv7vRG0tHN15n9HhZCXb0lwotNL32xMpa9NiMxMPTTO5NVvRI8Pn3213EQ3KRqWpkLu0nIK1S1BUhb7DTfQfbsLf0EJy2NjNfcX0JfxRLIWLf8L060kyJLKGb9lmytUP0qX+L33H/mR0OFlHURV8N9ait4QZaRxMSZv59bUMHG6a1rmWNZuJHXnlvPvjoSB2X1lK4hGjXMX5FKyuwbe6Bk9tGYHeHnr2HTc6LDEFbSSx6OsJXYgkQyKr1F15L7aRChqPfdXoULKSfbUPzWMivD81E24VRcFe4CV4fIqNWU+fC4zueC8WjMlsJn9JJUlrkkin1ClKV8lQHC0Lh8fOkGRIZBWT2UJF/gcZdu6WydQGUBQFe7WX4abBlLXpXVJK0mxj8GjTlOdaVm0mdlAmTC+0YF8vviVVWD1OkkGZn5VukkMx9LiG2ecwOhTDSDIksk7lpndjjuVx6tADRoeSlRyVXpT+WErr0+QtKUZ1uek/eHLS81SH87zJ0rom9afmWzIew+pwYnJZ0HWdeG+YZGjq+lBi/umajhZJZG2P0BmSDImsY7LaKbG9E7/1acID6b/f0nxvfLrQLGUulISe8l2wPZUFqBYLkb7JV6uZy5eRaDoMQKjtGBaXN6VxZKrhgX4GOtrwd7bj7+ogMjyUkna1ZBL1nLIGZq9tbIJuvDdMvDe86DcBTVdjFaYLs7dH6AxJhkRWqrn4LtDMNO3+ltGhTCr4XCtt//xHwnsXT1E7a0UOugqDvzlJ5HA/kYN9KbkY6ppOwpmL3ts54fGE30+8uwdTcSXJgW5C7cfRknFyatbO+bkzXaCnG9VkIr+sgrzScvJKyoiPRFKSEAV6uvAUFp93v8llwVLoxFzgIDkUI9EXOXsbHFl0HwLSUaIvkjUVpqdiNjoAIYxgy8mnUL+Jbn6O++WVVFz8blR19LNBbHiQ5t3fp2Ldn+HIN26VUbQ5SPC3TSjAwE8aCHcGKLh5GjV10pxqN2NencvIIT/RY34AdIeK+6ISogMhEiMJSt6xCnPB9D6thgZHl21HwwkKKtzo8SWMNBzDWlWJ6jjbRtLvRzn9b6zmFKAP+zF5fPQfeB53xTJseaUA+Ls6yCvJjtVl4WCAyFAQd14+Nqdr3DFPQRH+znaioRCqScVktuDKzZtR+7quo2napBdbRVEw543fBFRPaCT7RxMi1WbC5MnuIZz5oI0kUG2mrKkwPRVFXwTpdzAYxOv1EggE8Hg8RocjMkQ02Mf+5/6eoHMXOeFNrL70fvwtr9LYdz9JawBzLI+qnLupuvQDmCzTezMeGjrCy6+8hUsufgyPp37WselJndYvvYBiMVH6wfV0/XA/CS1JzScun3Wb6UZPaKObt+ow+HwrkYN9KPlW8MdRnCbK//5iFMvky3zDpyfjmiwqigJW+9nPd/GODrRoFEVR0DUdc34eyUAAa/XorvXR3U8TstvwLtvEYMNL+OqvIhoOERkaAl0nt6R0/l58Ghjs7sJis02Z4CQTCTQtSTIWJzToJ8fnw+qYXh2agY42cktKUdXZL9fWRhIkh+Og65hz7SgWGdBIhXhfBMs0P3DMp3S5fkvPkMhaNk8Bl7zlIdp2/5zG6HZe2n8DqBqexGZqav6S5sb/4WT0C7Q89SCWZDEWvNgtJVithThcFVRc/J6x3qRUi54KoA5qFH2knuRQDLpjFL135bw8l1EUszo2d6Tw9hVw++iGrvGuEF1f20P/r05Q8I4VF3x8Mq4xEoqTX+qa8Lil7PzenWTg7NJuxWIjr24jitWOq6SG4fYTRBImCquX4O9sJ5lIYDIvvrdIXdfpa20mt6gEi90+5fkmsxkTZixWG3a3m2BfL6HAIABmqxV3nm/Cnp9kIo6iqnNKhGC0J/HMPnexzhCWEqcM64iUW3x/6ULMUMWm2ykavoYTu/4Tk+Jg2Y2fwGS2ULjyKgZO7qbt2I+IKwFi+iCDiZdIMIimhxnY8TIbbkptvaJkIEoyGKP3ewdQAEuZm56fHiKRr+BYUzDl4xcDS4mL3BuXEHjiFPGrKieshqvrOv7uEAUVObN/njWXEd//R6wXXYW9qJqBg3/EmT/aG5RbUkZfazOFVTWzbj8dxaMjDHZ1UlBZPTZkOFOegsLx7XWfnqOl6+O2PUnG4/gqquYU7+tZip3EO0OjyZFJweyV4bPZknRyPEmGhACs7lxWX/vZ8+7PX7qJ/KWbzrv/2DNfpNX6bfqO/RkFy98w5+fX40m6Hz1M4rVBYPSNKueqChSTQrjFj3dFSVaN7Ts3FhN44hT9e3sovq76vJ6Awe7wBXuEJnXOrADFZELXtbGLeH7NCjBZRo8pCrlFJfS3t2K2WPEWnT8BONMM9feRjMcprF6SsjYtNvuCzq9SVAVLqQuSOnpSJ94TxlKUfVtHpELGz49JMUmGhJiFmkvupvfZP/Ba6wcobvozVl71acy2mY2/67qOFowR7w7T99QJtI4I+W+rw5RjIdE/gvuKcrRYEvMg2Mpm3wOSibSh0blAOeVuBjpDmC0qZqsJp8dKNJzAYjOhmmbes6Enx9cUstRtIt7wCpaVl8JIAAqWnT1mt+MrryQaDhPs7cFTWDS3F2WQvpYmTBYLTm8eNl/mJw6KooBZQTGD7jSTDMUxuSxGh5VRtFgSVeZejSPJkBCzYHXnctkNv+H48/9Bh/oDhv9wmEuu/99pPdb/TBODB9ox9Wuop/ev1NwKxR9ej61mfM2beG8ERYd4fwQ9qaOYsqN3qP+3J8Brxrk8H5dFRdN0EtEkg91hLDYT7ryp57pMxFJaSryjY2w+kZrjITFyZqPXiT8r25xOYpEwkeEhHO7MSkqDfT3klpRhtlqNDmVemNxW4t0hSYZmSI8kUJ3yMzuXJENCzJLJYmPlm/6J/MObOdDxVxz/439Sfsltkz4m1jpEaEcrjmoXzq0FWIpdWIqdmPLsEw6DWYqd2C8rYmhnK4FDneRvrsJ9efmiToqSQzESx4LkvWP52MohVVWwOsxYHXN7y1KdTuJd3eixGMq5CYKuw0gQkgkwnf8cOb4C+lqbsbvcGTV5N5lILNpE6AxzgSNtVkaJzCX9ZELMUdHqa/DFb6A7+svROSiTGNrXhWaF4g+vx3N1FY7VPsw+xwXnAylmlYK3raDorzdgLnAw+NuTdD16cFEXpDuzd5WleH6GdGxLlxBrbT17h6pCfyOUbZgwETojr7ScgY62eYlp3izi35MzFJOaFa9TzC9JhoRIgZLKW0jY+jn60r9MeFzXdSIH+wi91Inn8oqx+jlDsSGaAk2cDJzkhP8EwViQSOL8bSpsVR7KP3gR+e9YQXLvIANPNs7r6zFSMjg6dqh65q9Hw5SbS3Jw8Jx7dJhiCbjJbMbhziEczJyd1xdz0nwuxayix2VLj+nKjt+KmZFhMiFSoGj1tQz1/y0tfA2A4Zc7MReVjr5JJzWCL7eTOD6EaZmbvg1xgsEW4locr81LjbcGGL1wnQycJKknqcutm3A4xrWpmGQoTvC3pwh67HjeWLGQL3NBJPpG0M0Kppz5S4bMPh+xpiZMublocQWcvmk9zunNZbC7a/RrjxctmaSn6STFS5el3fDZUH8f7rx8o8NYEKZcG4neiKwsE7MmyZAQKaCqKnVXfZScg6vpaHyM+H4LA6GjZ0/wmvG9fzX21fmjFZF1/byLp6Io1ObWktSSnAqcYmnu0gmfy3NlBdH2IIO/P4X7klJU29yK2qWbRF8Y8szzXkpAsViIHj+OddVF4Jz+RTS3uIRh/wCDXZ3o6BQtWYq/s538svRJTIN9vaDr064UnekURUGxmUa3mLDLZU3MnPzWCJFCJWuvp2Tt9ei36OixJHpcQzEpKPbxF/fJehFMqgmPzYN/xE+efeKtEvJuWErkYB+Bp5rIe3Ntyl+HkSJ9Q1gL5v8ibikvn/Vj3Xn5xKMjBHt7UVUTZouVeHQEi212q9xSaaCjDYfHm3Er3+bK7LUR7xtddSmry8RMyZwhIeaBoiqodjOmHCuq0zLjXo4CRwEDIwMXPG7Ot+PaVELo+Y6U7PieLnRdJ9kRwV6S/nsMWmx2fBWVAHgKixjq7zc0nnAwQH9bK57C4qxLhM6wFDjgzJ53QsyA9AwJkaYqcipoH26n3D1xD4at1E2EbkJ7unFfujg2FU10h1EiGtaqTLyY6xMOf6b0GXSdob5ekskEWiIBKKe31dCxu3PGkrNsZvLa0EYSstxezIgkQ0KkKZvJRiwZu+Bx12WlhFsGGfjVCaxlbqxz2KcrHUSbAvR+cz9ARk6E9RYVE+juIqeggGQ8Puv5OolYjIGONlRVHSuYGBkeIjzoRzWb8RQUYjLLMNBkVLsZLRTPqkKlYm5kmEyINFbgKKAl2DLhMUVRKHz7ChSfhc4f7kcbSSxwdKkV7wwBkP/uFZh9mfeJ3mS2kEwm8Hd2EOjpRtdmN3zp72ynqGYpBVU1hAb9+DvbiUci+CqqyCspk0Romkz5duJdoUU1jCzmjyRDQqSxHGsO+fZ8Ooc7JzyuWEyUfmA9SkSj9+dHMrqujBZNojtUnBsycw8wgPzScnJ8heSXVxLs65lVG2bb2Z3YvUXF5JWWZ+y+aEZSFAVLmYtEX0QSIjElSYaESHNuq5tI8vxCjGeYfQ58b1tO/MDgWO9KJtJHkmDN7CENRVWxOZ2YzGZ0beaJaSwSTosVaYuFoihYSlwk+iPo8eTUDxBZS5IhITJAsbOY7lD3BY871xeiWSB8xNgVTXOhx5JgXRxvSbGRCCbrzIaz4iMjBHq6s6ZQ4kKyFLtIBC48/06IxfHOI8Qi57K4CCfCFzyumFVsdbkM7m6bVY9EOtDjGpgzu2fojOGBAXLyC6Y9bJmIxQj29VJYvWSeIxNCTESSISEyhEkxkdAuPEnatbIA84DGyOHM7B2KdAQw59imPjEDODweBjraGezupL+tdcrzA73dsiw+g+iaTrw7RLwvQjIQNTockQKSDAmRISpzKmkbuvCu6Vokjq6Cbal3AaNKjYR/BK09gmf94qiX5HDnkF9WTl5JGapp8rfZwe4u7C73AkUm5kqPayR6wpiLnKN1jEwKyaAMwWU6SYaEyBBTFfPrf6UF66pcVGfmLb2OnhrdCd62LNfYQObBZCNlg91dOHJycOVOvO2KSB3FrMx5Z3s9qZHoj2ApcY39PZrcVrSRREav5BSSDAmRUVwWF0OxofPuj3eFsPTpeDaVGRDV3OhJjeCuNsgxL7o9pSJDQezuiXt9/J3tmMxmbE7XAkeVnUxeG4nBuW3TkeiLYC4+v5imucBBvDMkCVEGk2RIiAxS6CykL9I37j5tJEHPkw1oDgX78szrYfD/9iSJtjCF712NYl5cb0mRoSGcnvOHLftam3F6c8nxFRgQVXZSFAU0fdYJixaOj+4zOEEPraIqWIqcJP0yfyhTyXYcQmQYVVHRdA0loTP0QgeDzzRDXKfg3StRzCqRQ30E/tBM3B8h7+Za3JvTdx5OeH8v4T91kvuWpdhqMm+u02SSiTiqOnFyJz1CxjAXOkn0Rma13UtyOD7p4xSzip6U4o6ZSpIhITJMhbuC1qFWTD/rRT8Rxroxn8Lr6zB5bYR2d+P/2TEAFCDWOgRpmAwlh2IkB6P0/+Qotvp83G/IvOG9qfg72imoqjnvfl3XYR43cxUXpqjK6B/GDCUGRjB5rVO3ryjzvlmvmB+SDAmRYUyqiaSWxJnrJuQZofKdawGI94QZeOw4josKcK4uZOChI9hXpOewWed9LwGj1yXfrcsX3cUjHh3BNsEKMV3T6Gk6OWGSJBbGTH/XksMxFIuKapv6cqnmWNCG45hypk6cRHqRZEiIDFTpqaS99BjmV3T6HzlKIhYndjKI4jaRf9tyOh/YjanSiWNt+s1Jef0+Uapz8b0NmS1WEvHxy61HhocZHuijqGYpygWGz8QCmGHerYUT0x5WU21mEkMRyJlFXMJQi+9dSIgsYFEtVG1aycDJBiL7esFtwrnKh/uyMhSzitYZxXFZSXr2uJgUlGIbencUZ7rGOEeKqqIlk/S3t6IoKqBjsdulRyhN6Jo+OmQ22TkJjURfBFOe7BWXDSQZEiJDqTYTBe9bTaxjGJPHisk92jUfOdiHArg2Fhsb4AUoikLpX2/E/5tGwru6MOfZ8Vy1+Kov+8oX32taDEweG8lgDHPuhaud60mdRN9oPSGRHSQZEiLDWcvGz03xP9+MWu3CVuUxKKKpqTYz+W9fjt9uIvhkE+Hj/RS8bQXmAofRoYnFTgG48PL6M1ttWEpnlwgpVhUtlkS1mmYXnzDEjAauH3jgAerr6/F4PHg8HrZs2cKTTz45dnzr1q0oijLuds8990y7/XvuuQdFUfjKV74yk7CEEOfQNA2zJ/33+FIUhbyba8l71wribSH6ftlgdEgiG5gUSJxNhpKhOPG+yNgt0RMeV2F6xs2f7nkSmWVGPUMVFRXcf//91NXVoes6P/jBD7j11lvZu3cva9asAeCuu+7ic5/73NhjnM7pTTx77LHH2LVrF2Vli2+JrRALyV1bwNCL7ST6I5h96d3ToigKrouKUMwqAw8doeMbu/G9pQ5bdfr2aonMpigKujY6DAajE/gtKe6RVBgtHyGryjLHjHqGbrnlFm6++Wbq6upYvnw59913H263m127do2d43Q6KSkpGbt5PFO/qbW3t/O3f/u3PPTQQ1gsi6scvxALLeeKClS3ha5v75vz9gMLxbHWR+7blqEHYvR8+zWSQ/LJWswfS5ETc4EDc4FjXvbyMxc4QBndJifeGz7b6zQwIlt2pKlZr+9MJpM88sgjhEIhtmzZMnb/Qw89REFBAWvXrmXbtm2Ew+FJ29E0jfe///3ce++9Y71LU4lGowSDwXE3IcQok8tCyd0bQFHofvA1ksH03yJAURTcl5VS8nebAOj530Ny0RAZzeS2YilxYSkc3d3eUuDA5LGS6ItkzIeUbDLjZOjAgQO43W5sNhv33HMPjz32GKtXrwbgjjvu4Mc//jHPPPMM27Zt40c/+hHve9/7Jm3vC1/4Amazmb/7u7+bdgzbt2/H6/WO3SorZdWGEOcyeW0U/0U9eiRJ55deIdqSGR8YTG4ruTcsIXlyGC2cMDocIVJKMatYCp0oFhPJQPp/SMkmij7Dj1+xWIyWlhYCgQCPPvooDz74IDt37hxLiM719NNPc80113DixAlqa2vPO757927e/OY3s2fPnrG5QjU1NXz0ox/lox/96AVjiEajRKNnf5GCwSCVlZUEAoFpDcsJkS20cJyuB19DH0lQ/OENmPPTv2ZKrHWInm/sI//dK3BuKDI6HCHmRcI/gmozzcsw3ZTPHYhiclpQLMYX/wwGg3i9XsOv3zP+SVitVpYtW8amTZvYvn0769ev56tf/eqE527evBmAEydOTHj8+eefp6enh6qqKsxmM2azmebmZj7xiU9QU1NzwRhsNtvYirYzNyHE+VSnhcJ3rUKPanR9bTfxnsmHrdOBpcyFqdrFwCMNdH57L1pEeojE4mPOs5McjhsdhjhtzmmhpmnjemnOtW/fPgBKSyfeKPL9738/+/fvZ9++fWO3srIy7r33Xnbs2DHX0IQQgKXYRdknLgG7iYHfNRodzpQUk0rJhzeQ/64VJNpDDO44ZXRIQswLk9siiwXSxIyW1m/bto2bbrqJqqoqhoaGePjhh3n22WfZsWMHjY2NPPzww9x88834fD7279/Pxz72Ma688krq6+vH2li5ciXbt2/ntttuw+fz4fP5xj2HxWKhpKSEFStWpOYVCiFQnRY8m8sJ/q6JkWN+7MvTcwPXMxSLivOiIsLHBgjv7cZ7XQ0ml6w0FYuL6rQQ7wnLEvw0MKOeoZ6eHu68805WrFjBNddcwyuvvMKOHTu47rrrsFqtPPXUU1x//fWsXLmST3ziE9x+++38+te/HtdGQ0MDgUAgpS9CCDE1LTzaJT/w2xPoWmas1HLVF6IDXf+9x+hQhJgXplwb8d4wCb+sMDPSjCdQp6N0mYAlRDqLHOyj/8dHAMi7vQ7XJSUGRzQ9wy91MvjYCXwfXINjZb7R4QgxL7RYkuTACOZCJ4ppfjcvlgnU5zP+JyGEWBCOtQWU/9sbsdXn4//dSbSRzJiY7Lq0BDxmBp9uQotmRsxCzJRqNWEudpLoC48WauwNk+iPjN76Rm/xvtHvRepJMiREFlFUBdfaIggliZ4YnPRcXdfRYsmFCWwSiqJQ8K5VJLrC9HzvwNg2CkIsNoqiYCl2YfY5sBQ6Mfsco7fT1bJHCzfaiHeHxoa9RWpIMiREFtHCcQaeOIFa6cS+avIhp4FfHaf9My8QPtC7QNFdmL02l4J3ryLeHqLzP15l8Olmo0MSYt4o6oWHyRSLiqXYhRZJoEXHf1jRNV0qt8+SJENCZJHhFzogkMBVm094Tw/R5iChV7sI7e4m/FrP2NYd4b09RHZ1o+jQ//NjxLtCBkcOjtU+yrZtxr7Uy9DuDqPDEcJQZp+D5DmTrhP+kdEhtd7RPdDEzMxoab0QIrOZ8kYrUA8924augKKDzugu2wC6WcFS5iTREkIptFFyz0V0PbCHrm/spfDONdjrjF2Sb3JZMBc6YTj9i0cKMd/MBY7RQqrK6FY2qmP0kq7FksR7R/9GzHl2FLP0e0xFkiEhsohzYxEmz+gGkopFJTEwgtnnQFEVtGiC4HNthI73kffO5TjrC1EsKmV/fzHdPzhA38OHKf/nLYa+sY4c8xN6rRtrrawaFUIxq1iKnOfdr1pNqIVOdF0n6Y+ix5Ojf+eSFF2QJENCZBFFUcb17ljL3GNfmyxW8m5eSh5Lxz/GYiL36hr6vnOAhH8ES+H5b77zJdY6RO9vjmHJteO5tJze7x1EcZsouFWKsgoxFUVRxvYjjPeGUW1mTB4p8DgRSYaEEFOKnhwtlKooqa1/oidHJ3ueqaui6zr6SBLFZiKyv5f+XxxD9ViJ9QXoe20AxapQdu9mVJsppXEIsdhZCp0kQ3Hi3SEU+fs5jyRDQohJhV/rJfh/LTi3lGDypW7Xe13T6fzaqyguM4XvWIUeTdL7k8NonSNgUiCpY13hpfC9a0DTiTYGMHmtkggJMUsmlwXVYSbWEsTklh6ic0kyJIS4oGhzkP6fHMW+3kf+LctS0jOkazracJyRYwNoXaOrXrq+8MroQRU8N1QTbh7Es6EEx/rCsed0rPFdqEkhxDQpqoKtxmt0GGlHkiEhxIR0Xafv0SOYSh0U/NnKSWufTFe8N0zP9/aj+0cLxllWeHHWjSY5llIXphwrliInHqrm/FxCCDFdkgwJISaU6A6j98bwfWgtimnuq1C0WJLehw+j++Pkvq0Wk9eGfUV+SpIsIYSYC0mGhBATCu3pRrco2JbMfRn78MudDP72JHpCp+ADq3GskiEvIUT6kGRICHGeRCDK0J/a8WytRLHMfsKyrumEXu5k8JeN4DJR8pGLFnRpvhBCTIckQ0KI80SP+SEJOVdUzKmdoefaCP6uCbXcQclfrEd1WlIUoRBCpI4kQ0KI88R7wyhAwh/FWjq9t4nIoX6iJwex1eVhKXISeq2HoR3NOC4vwffWuvkNWAgh5kCSISEE0ZEE/7ezieuuXoLJrPCVQ+28QgjLI7unVaAt2RvmryJm1mFm+E8d/McKGwdzTfzk4iLy3rJsAV6BEELMniRDQmSxZ59voq07xHPHe/l9IET9Cyepdtn59cAQ11bk4StxT90I8JOWQf7TorHjM1cR7wzhe6GRA7mQe/kyWS0mhEh7kgwJkaV+9dtj/P1zxwFQgcucdoLxBL8eGOKjK0r46J9vmnZbP3m1FZfbhmJSsVbk8Kk/28Cn5iluIYRINUmGhMhSzxzupsZk4oltb0JVweEcLc8/6I+Qm+eYUVtW4Kby3NQHKYQQC0CSISGyUDyWZGffEDeX5eJ63R5FM02ENE0jBtjMsmeYECIzzb2srBAi4/zk8aP40Xnb5dVzbisWTQJgt0oyJITITJIMCZFl9u3r5DOvNvHmfDebNpbOub3ISAIAmyRDQogMJcmQEFnm1y+04FEU/vOjb0BV5/4WMBI5nQxNYwm+EEKkI0mGhMgyB/uGWOGwYrWmZsqg3x8BwPO6uUdCCJEpJBkSIovseqmNveEob6jKT1mbTe1BAGoqvSlrUwghFpIkQ0JkiZ7uYd732GuUmUy879ZVKWu3rTeEBSgpyUlZm0IIsZAkGRIiC/j7w/zL93eTAH5492UzXj4/mdb+MMWqisksbydCiMwkdYaEyAL3fvslngqE2WS3UlWdm9K2W4cilNrkrUQIkbnkHUyILLCy0M1TgTC7R2Jcuu23rHHbqfI6qfI5uePWlWPVp2ejIxJjbd709jATQoh0JMmQEFng4x/axLX7uzly0s+xziCvdgfZ1dZPpK2fnx3u5Hefv2HWbXcmNG7MTd2wmxBCLDRJhoTIAqqqsmFDKRs2jC+y+K8PvMT/NPfx40cP8Z63rZrxvJ/BwQhD6FQWOFMZrhBCLCiZ8ShEFvvo+zdwQ56bT73axM2f+QOaps3o8c3NAQCqSmUlmRAic0kyJEQWc7tt/NcnrmC9zUpDIsGePV0zenzf6YKLieTMkighhEgnkgwJkeXMZpUvvGcDAI/vap7RY99waQV1ZjP/8OtDDJ5OjIQQItNIMiSEoKLCQ5Gi8vv2QTo7hqb9OLvTwrf+/BICms7ffePFGQ+zCSFEOpBkSAiB223je+/dSK+u8Ys/nJjRY5fW5vPFq5fz3HCE//r+3nmKUAgh5o8kQ0IIANauLeaNbgc/PdY94x6em29Yxl9U+fivY13sfH5mQ21CCGE0SYaEEADEYgniuk5nMkkyoc/48dvuvpSNDisffeIQHac3bxVCiEwgyZAQguhIgi3/8nteCI3w6U3VWKymGbdhNqt8454tmBW459u7iMUS8xCpEEKkniRDQgh0TadfH+0NumRd8azbKS5287W31XMoGuez33w5VeEJIcS8kmRICIHdaeGf1lUA8Lmf7p9TW5dtruDeNeU81OHnF79pSEV4QggxryQZEkIAcPd71/Op+gpeCI/w4X97Zk7DXHe/t57rvE7++Y8n2PfazAo5CiHEQpNkSAgx5qYra1htsfBUMMyjv559r46qqnz5I5dTbTFzx//u5pmdp1IYpRBCpJYkQ0KIMeUVXr7yvo0AhOc4ATrHY+Nn926l3mnjricP88hjh1MRohBCpJwkQ0KIcb7yy0MA3Hzlkjm3leOx8cN/3MpNPjf/+NIpvvSdV6RKtRAi7UgyJIQY0905xO/9w/x9XTFl5Z6UtGm1mvnqJ67gI7VFfKOxh4/+xx9l2b0QIq1IMiSEGLPnYA8J4O3X1Ka0XVVVufeuS7h/8xJ+2z/E+7Y/S2BwJKXPIYQQs2U2OgAhxMzEY0m+88h+ApE48aRGLKETTY4OPSlAjs3Mx++8CJfbOuO2+wOjCYrVOj9vDe++bTUlBU7+5olDvP1Lz/I/f7mFqirvvDyXEEJM14x6hh544AHq6+vxeDx4PB62bNnCk08+OXZ869atKIoy7nbPPfdM2uZnPvMZVq5cicvlIi8vj2uvvZaXXnppdq9GiCyw808tfPFwBz9v6uN3bX6e7x5kX/8Q+/qH2NEd4HstfQSD0Vm1XVHsAqDhRH8qQx5n6xU1/PTOSwhpOm9/4E+y9F4IYbgZffyrqKjg/vvvp66uDl3X+cEPfsCtt97K3r17WbNmDQB33XUXn/vc58Ye43Q6J21z+fLlfP3rX2fp0qVEIhH+8z//k+uvv54TJ05QWFg4i5ckxOK253gfOSi8fN+NqOr4zzPXfup3rLNZKC3LmVXbO/d3YQYu2ViWgkgvbPXqIn75d2/kg//9Au/53938V99Krkvx0JwQQkzXjHqGbrnlFm6++Wbq6upYvnw59913H263m127do2d43Q6KSkpGbt5PJNPwrzjjju49tprWbp0KWvWrOHLX/4ywWCQ/fvnVgVXiMVqb1eAtU7reYnQkSO9nEgkuW1D+aza7esN8UhrP28v9s5qiG2mSkpz+Nm9W9nksvPXfzjK/v3d8/6cQggxkVlPoE4mkzzyyCOEQiG2bNkydv9DDz1EQUEBa9euZdu2bYTD4Wm3GYvF+Pa3v43X62X9+vUXPC8ajRIMBsfdhMgGmqZxMBxjY+n582w++7PX8CkK11xRPaM2e7qH+dmvjnDPf78AwN++Y11KYp2OHI+NBz9xJVUmE3//k71EwrEFe24hhDhjxrMkDxw4wJYtWxgZGcHtdvPYY4+xevVqYLSXp7q6mrKyMvbv388nP/lJGhoa+MUvfjFpm7/5zW9497vfTTgcprS0lD/84Q8UFBRc8Pzt27fz2c9+dqahC5HxThwfYAidjXXn/30ci8R4a0U+ubmOabf39e/v5d+PdgDgBv77ptVUVi7shGa708J/vecibvvxq3zm26/whY++YUGfXwghFF0/vVX1NMViMVpaWggEAjz66KM8+OCD7Ny5cywhOtfTTz/NNddcw4kTJ6itvfB8gFAoRGdnJ319fXznO9/h6aef5qWXXqKoqGjC86PRKNHo2QmiwWCQyspKAoHAlMNyQmSyh39+iH9+pYm9//im85Ket/3L74lpOo//y3WYzZN3+iYSGtv+6wV+1hOgymTi6++6iDWrCzFN8bj59M0f7+P+g+1889qV3HitzB8SIhsEg0G8Xq/h1+8Zv/NZrVaWLVvGpk2b2L59O+vXr+erX/3qhOdu3rwZgBMnTkzapsvlYtmyZVx22WV897vfxWw2893vfveC59tstrEVbWduQmSDl072U2UyTdj787FrV3A0nuCfvvbClFWef/7rBn7WE+DumkIev/cq6uuLDU2EAO6+o57LnDa2PdVAd/ewobEIIbLLnN/9NE0b10tzrn379gFQWlqasjaFyFaDgxF+3z/EjZX5Ex6/6opqPrW+kp92B3jsN8cmbavDH8YN/NM9l85oWG0+qarKV+7ajA58/NsvybYdQogFM6M5Q9u2beOmm26iqqqKoaEhHn74YZ599ll27NhBY2MjDz/8MDfffDM+n4/9+/fzsY99jCuvvJL6+vqxNlauXMn27du57bbbCIVC3Hfffbz1rW+ltLSUvr4+vvGNb9De3s473/nOlL9YITLZw48fJQZ88K2rLnjOB9+1ludO9vGJFxp5dF87K3wuApE4bquZy1cVcdN1o8NP/cOjHzY0TTtvVZqRSkpz+Lc3Leevn27gp786yrtvO3/4XQghUm1GyVBPTw933nknnZ2deL1e6uvr2bFjB9dddx2tra089dRTfOUrXyEUClFZWcntt9/Opz71qXFtNDQ0EAgEADCZTBw9epQf/OAH9PX14fP5uOSSS3j++efH6hYJIUaTlkcbutnqcU5aQ0hVVb77ya186+HX+NXxHna0+fGYVAJJjR91DPBnBzvZUJXHLzr8vLvSl1aJ0Bk3X7+Mra+08PVXmnnnLSsNH74TQix+M55AnY7SZQKWEPNl9+4Obv/ZXr513UpumEVxQk3T+Mw3XuKH7QMArLdZ+M5fX05RsTvVoabEiy+18Z7HXuM710kxRiEWs3S5fsveZEJkgJ88d5ICReGaq5bM6vGqqvLpv7yUTU81cumG0llXqF4omy8pY/mvD/H9F5okGRJCzDvpfxYizY2MxHmyO8gtFflTLpmfjMVq4tabl6d9IgSjydv715fzQmiExnncJ00IIUCSISHS3rN/bGEInXfMslcoU73jzcvJQeH7T06+Mk4IIeZKkiEh0tyO1zqoUFVWrc6ujYsdTiu3VeTxWPsA0ZGE0eEIIRYxSYaESHPP9Q3xptLctFz5Nd/Kch2EgUgkbnQoQohFLPveXYXIMBFdp8BtMzoMQzx9so8Ndiu5eelRGFIIsThJMiREmorHkvziNw2Egcoil9HhLLjBwQi7w1FurJt4j0IhhEgVWVovRJr69H/v4pGuQQoUhc0bZralzWIw0B8hATisJqNDEUIsctIzJEQa8veH+VnXIH9fV8zL991IWXn2FRNdWpvP1hwH39jTKhOohRDzSpIhIdJQc1uQJHDF+rKsnDgN0NISQNOhS9f43k8PGh2OEGIRk2EyIdJQKBQDwO22GBzJwmtvC/DEzib+40AbsdP3PXikg79ig5FhCSEWMUmGhEhDiqIAEIlk1/DQoD/CVV//IwmgXFX5nw9cwnAohmrKzt4xIcTCkGRIiDS08aISTL/az+6jPVx00eKdPK1pGv39EQoLR1fLuXNsmIHr89z89yevMjY4IUTWkGRIiDRkt1vIQaF/KDb1yRns41/+I7/sG6JcVfmPW9fR2BZgudXKb/3D7NnTwcaNZUaHKITIApIMCZGGBvrDDKKzpMhtdChzpmnahJPAkwmNVwZCqEC7pvHux14bd/ztP93LcwUuqqq8CxSpECJbyUC8EGnoe48dxgJs2ZTZPSP/7+svsOqfnmTv3s7zjn3nkf20axrfvWk133/zGhzAXdUF/PI9m7jWOzps9p1fHlrgiIUQ2Uh6hoRIQz880UMc2PF8EzdcUUNOjpXc3MzZkkLTNN5/3zP8KTQCwG0/2UPNoyaqHVZuXlPKm69bylcOtvOOIi9XX7UEgINbqjCZRz+fPbi+hOPH+ynIz5zXLITIXJIMCZGG3ru0gBfaA/zr/jb+dX8bG+1WfvGZ64wOa9o0jbFEyAp8sKaQnuEoxwJhPvnSST750kkAbrmkcuwxZxKhM+rqfAsWrxAiu0kyJEQa+uTdlxKLJfj8N1/hRx0D7BmJ8csnjvG2Ny83OrRpMZtVPljp4/ut/fzmzzezfEXB2LEXX2rj0Ml+NA2ueEPlJK0IIcTCUHRd140OYq6CwSBer5dAIIDHk33bFojFbSQc5y+//DwvD0f4v7+9Ii235jh4sJvtjx3k8qo8/ur9GxgejnHd9mdY5rTy0KevMTo8IUSaSpfrtyRDQmSA9rYAW7/+R+JALgo3FHv5i5tXjOtxMcqxhj6u/5+Xxr5fYjLx/7d390FNnfkewL8JmAhCENcAChGhLVzU+tpaaQtUtHArVmbV1pFLxfduO7OjHde2XLxF3XVGKzPd3hY72nZRSrdaandK1ZVyBb2dgbFIwRdetC2+XRGoFATfeEme+4dLRuTFxJzk5JDvZ4Y/knPOc37Pd0LOj8M5yU2TCY1C4L+jH8O8Oco4m0VEjucsx2/eTUakAIFBPsj+/SQAQAsEDjS0IDHrOD7cU47rLXdkre2/9lVghEqFzdPG4C9PjEWd0YgpPp7IS3qCjRARKQLPDBEpyK+/3sTt210YPlyLtz86jkPNN2BQuyH3j88gYJS3w+tpv9OF8RvzsW5cIF5bMhkA0NlhxBCNm8NrISLlcZbjN88MESmIXn/3Qwh1uqHY8VYMDiY/gcsmI979+0ncudPp8Hqqz15DF4BJ99z5xUaIiJSGzRCRgo2f4I/kQF/849frmPPnI6i70urQ/TdcuwkAGB2g/E/KJiLXxWaISOH+8sensW/BJDQbTZj1wfd4desxpL1fjKt1bXbft+Ff/5q7ctX++yIishd+zhDRIDD9ySAUho7A9pwK/ONqM263AD/sKEbK5CAM99LAaBKYFR0MLy/tgON0dZlQUVGPqVMDoFarcTD/J5yvv4H/mBsO39959lpfpVIBAJpkvoibiMgWvICaaJAxmUwoOf5/+POhapzt7EL3L/hwqLDi3wKwfOEEDPPS9Lnt3/aewuaKy/hDiB5/WvEEJm34J27+a9lYNzf8KeoRzP33xwAAVVWNWJR9Am0QOP2fs+GtG7jRIiK6n7Mcv9kMEQ1ijQ03UH2uCUO1btj7v7XIu9YGg5sb4seMwB8WTjCf7WluuoV3Pi3FhRsdON3RAQAYCuAOgI1TxkDjrsZXp+tQcacDc3/nDR/tEOyt+w2dAGK8PLBnQ6xscyQi5XKW4zebISIXcuJEHf76z2qU3bwDDVSYOdIb/t5afHOhCfXCBAB40kODeeNGobbxBiaO9UXiC49BrVajs8OIzJwK/P2nBjQKgQV+OqSvfBJeXhqo1bz8kIis5yzHbzZDRC7o55+asDX3FP6n9Zb5uekeWiyaEoTpU0bBYPDpd1uTyYSuLhM0Gl5ySES2cZbjN5shIhfWfqcLdXVtGD3aG0M0ap7hISKHcpbjN/+0I3Jh2qHuCAn1lbsMIiJZ8c9AIiIicmlshoiIiMilsRkiIiIil8ZmiIiIiFwamyEiIiJyaWyGiIiIyKWxGSIiIiKXxmaIiIiIXBqbISIiInJpbIaIiIjIpbEZIiIiIpfGZoiIiIhcGpshIiIicmmD4lvrhRAAgNbWVpkrISIiIkt1H7e7j+NyGRTNUFtbGwDAYDDIXAkRERFZq62tDT4+PrLtXyXkbsckYDKZUFdXB29vb6hUqh7LWltbYTAYcPnyZeh0OpkqdD7MpTdm0jfm0hsz6Rtz6Y2Z9K07l0uXLkGlUmH06NFQq+W7cmdQnBlSq9UICgoacB2dTscXYh+YS2/MpG/MpTdm0jfm0hsz6ZuPj49T5MILqImIiMilsRkiIiIilzbomyGtVov09HRotVq5S3EqzKU3ZtI35tIbM+kbc+mNmfTN2XIZFBdQExERET2sQX9miIiIiGggbIaIiIjIpbEZIiIiIpfGZoiIiIhcmuKaoS1btuDpp5+Gp6cnhg8f3mv5yZMnsXjxYhgMBnh4eCAiIgLvv/9+j3WuXr2KpKQkhIWFQa1WY+3atVbV0NTUhKCgIKhUKrS0tDz8ZCQiVyaWjCsnOV8rly5dQkJCAjw9PeHn54f169ejq6tLglnZRopMAODo0aOYOnUqtFotHn30UezevfuB+87Pz8eMGTPg7e0NvV6PBQsW4MKFC7ZPSgJy5iKEQEZGBsLCwqDVahEYGIgtW7ZIMCvbyZlLt59//hne3t597l8OcmVy9OhRJCYmYtSoURg2bBgmT56Mzz//XKJZ2U7O18qpU6cQFRWFoUOHwmAw4N1337W6fsU1Qx0dHXjppZfw2muv9bm8rKwMfn5+yMnJQWVlJdLS0pCamooPP/zQvE57ezv0ej02bNiASZMmWV3DihUrMHHixIeeg9TkysSSceUkVy5GoxEJCQno6OhAcXEx9uzZg927d+Odd96RZF62kCKT8+fPIyEhATNnzkRFRQXWrl2LlStXIj8/v9/9nj9/HomJiYiNjUVFRQXy8/Nx7do1zJ8/X/I5Pgy5cgGANWvW4JNPPkFGRgZqamqQl5eH6dOnSzq/hyVnLgDQ2dmJxYsXIyoqSrI52UquTIqLizFx4kTs378fp06dwrJly7BkyRIcOHBA8jk+DLlyaW1tRVxcHIKDg1FWVobt27dj48aN2LVrl3UTEAqVlZUlfHx8LFr39ddfFzNnzuxzWUxMjFizZo3F+92xY4eIiYkRR44cEQBEc3Ozxdvam1yZWDquXBydy6FDh4RarRb19fXm5z766COh0+lEe3u7RXXYmy2ZvPnmm2L8+PE91lm0aJGIj4/vd4zc3Fzh7u4ujEaj+bm8vDyhUqlER0eHdcXbkaNzqaqqEu7u7qKmpuah6nUUR+dy77bJyclW7d9R5MrkXnPmzBHLli2zaht7c3QuO3bsEL6+vj3eW9966y0RHh5uVd2KOzP0MK5fv44RI0bYPE5VVRU2b96M7OxsWb9QTgpSZeKocR1FivpLSkrw+OOPw9/f3/xcfHw8WltbUVlZaWuJDnd/JiUlJZg9e3aPdeLj41FSUtLvGNOmTYNarUZWVhaMRiOuX7+Ozz77DLNnz8aQIUPsVrs9SZHLt99+i9DQUBw4cAAhISEYO3YsVq5cid9++81uddubFLkAQGFhIXJzc5GZmWmXOh1JqkweNK7SSJFLSUkJoqOjodFoemxz9uxZNDc3W1zLoPii1oEUFxdj3759OHjwoE3jtLe3Y/Hixdi+fTvGjBmD2tpaiSp0PKkycdS4jiJV/fX19T0aIQDmx/X19TaN7Wh9ZdLf/FpbW3H79m14eHj0GickJATfffcdXn75Zbz66qswGo2IjIzEoUOH7D4He5Aql9raWly8eBG5ubnIzs6G0WjEG2+8gYULF6KwsNDu85CaVLk0NTVh6dKlyMnJcYov8bSFVJnc78svv0RpaSl27twpec2OIFUu9fX1CAkJ6bVN9zJfX1+L6nGK0xtvv/02VCrVgD81NTVWj3vmzBkkJiYiPT0dcXFxNtWYmpqKiIgIJCcn2zSOpZSQiSPGvZ/ScnEEJWRSX1+PVatWISUlBaWlpTh27Bg0Gg0WLlwIYacPwVdCLiaTCe3t7cjOzkZUVBSee+45fPrppygqKsLZs2dtGrs/Sshl1apVSEpKQnR0tE3jWEoJmdyrqKgIy5Ytw8cff4zx48dLNu79lJaLLZzizNC6deuwdOnSAdcJDQ21asyqqirMmjULq1evxoYNG2yo7q7CwkKcPn0aX331FQCY38BHjhyJtLQ0bNq0yeZ93EsJmdh73L4oIZeAgAD88MMPPZ5raGgwL5OaozMJCAgwz6dbQ0MDdDpdv3/RZmZmwsfHp8ddHjk5OTAYDDh+/DhmzJhhVX2WUEIuo0aNgru7O8LCwszPRUREALh7R2J4eLhV9VlCCbkUFhYiLy8PGRkZAO6+35pMJri7u2PXrl1Yvny5VfU9iBIy6Xbs2DG8+OKLeO+997BkyRKrarKWEnLpb5vuZZZyimZIr9dDr9dLNl5lZSViY2ORkpIi2S2q+/fvx+3bt82PS0tLsXz5cnz//fd45JFHJNnHvZSQiT3H7Y8ScomMjMSWLVvQ2NgIPz8/AEBBQQF0Oh3GjRsnyT7u5ehM+vr3VkFBASIjI/sd89atW72us3NzcwNw9+yIPSghl2eeeQZdXV345ZdfzO8j586dAwAEBwdLVvu9lJBLSUkJjEaj+fE333yDbdu2obi4GIGBgZLV3k0JmQB3bzufO3cutm3bhtWrV0tWb3+UkEtkZCTS0tLQ2dlpvv6woKAA4eHhFv+LDIDy7ia7ePGiKC8vF5s2bRJeXl6ivLxclJeXi7a2NiGEEKdPnxZ6vV4kJyeLq1evmn8aGxt7jNO93bRp00RSUpIoLy8XlZWV5uVff/31gFejFxUVOc3dZHJlYum4cpErl66uLjFhwgQRFxcnKioqxOHDh4VerxepqamOmfgApMiktrZWeHp6ivXr14vq6mqRmZkp3NzcxOHDh83rfPDBByI2Ntb8+MiRI0KlUolNmzaJc+fOibKyMhEfHy+Cg4PFrVu3HBdAP+TKxWg0iqlTp4ro6Gjx448/ihMnToinnnpKPP/8846b/ADkyuV+znQ3mVyZFBYWCk9PT5Gamtpj3KamJsdNfgBy5dLS0iL8/f3FK6+8Is6cOSP27t0rPD09xc6dO62qX3HNUEpKigDQ66eoqEgIIUR6enqfy4ODg3uM86B1srKyxEC9ojM1Q3JlYum4cpHztXLhwgXxwgsvCA8PDzFy5Eixbt060dnZaecZP5hUmRQVFYnJkycLjUYjQkNDRVZWVo/l6enpvbb54osvxJQpU8SwYcOEXq8X8+bNE9XV1fabrBXkzOXKlSti/vz5wsvLS/j7+4ulS5c6zQFOzlzu5UzNkFyZ9LffmJgYu87XUnK+Vk6ePCmeffZZodVqRWBgoNi6davV9auEsNPVi0REREQK4BR3kxERERHJhc0QERERuTQ2Q0REROTS2AwRERGRS2MzRERERC6NzRARERG5NDZDRERE5NLYDBEREZFLYzNERERELo3NEBEREbk0NkNERETk0tgMERERkUv7f4SFFlJrg0D6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if STATE == \"CA\":\n",
    "    tractToSplit = 5205\n",
    "    print(\"splitting a long border tract\",tractToSplit,\"that surrounds several coastal tracts\")\n",
    "    c = countyNo[tractToSplit]\n",
    "    for tt in countyTractList[c]:\n",
    "        if vtdGeom[tt].centroid.y > 35.4:\n",
    "            plotPoly(vtdGeom[tt],0.1)\n",
    "    xMin, xMax, yMin, yMax = -121.4, -121.1, 35.57, 35.8\n",
    "    PTS = [Point(xMin,yMin),Point(xMax,yMin),Point(xMax,yMax),Point(xMin,yMax)]\n",
    "    clipPoly = Polygon(PTS)\n",
    "    splitTractGeoms = list()\n",
    "    splitTractGeoms.append(vtdGeom[tractToSplit].difference(clipPoly))\n",
    "    remainingTractGeom = vtdGeom[tractToSplit].difference(splitTractGeoms[0])\n",
    "    plotPoly(splitTractGeoms[0])\n",
    "    xMin, xMax, yMin, yMax = -121.4, -120.95, 35.43, 35.8\n",
    "    PTS = [Point(xMin,yMin),Point(xMax,yMin),Point(xMax,yMax),Point(xMin,yMax)]\n",
    "    clipPoly = Polygon(PTS)\n",
    "    splitTractGeoms.append(remainingTractGeom.difference(clipPoly))\n",
    "    plotPoly(splitTractGeoms[1])   \n",
    "    remainingTractGeom = vtdGeom[tractToSplit].intersection(clipPoly)\n",
    "\n",
    "    xMin, xMax, yMin, yMax = -121.4, -120.6, 35.2, 35.36\n",
    "    PTS = [Point(xMin,yMin),Point(xMax,yMin),Point(xMax,yMax),Point(xMin,yMax)]\n",
    "    clipPoly = Polygon(PTS)\n",
    "    splitTractGeoms.append(remainingTractGeom.difference(clipPoly))\n",
    "    plotPoly(splitTractGeoms[2])   \n",
    "    remainingTractGeom = vtdGeom[tractToSplit].intersection(clipPoly)    \n",
    "    splitTractGeoms.append(vtdGeom[tractToSplit].intersection(clipPoly))\n",
    "    plotPoly(splitTractGeoms[3])\n",
    "    #plotPoly(vtdGeom[tractToSplit].buffer(0.01),0.1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 7 fragmented tracts. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 0\n",
      "looking for surrounds, now working on vtd 1001\n",
      "looking for surrounds, now working on vtd 2003\n",
      "looking for surrounds, now working on vtd 3008\n",
      "looking for surrounds, now working on vtd 4008\n",
      "looking for surrounds, now working on vtd 5008\n",
      "looking for surrounds, now working on vtd 6009\n",
      "looking for surrounds, now working on vtd 7011\n",
      "looking for surrounds, now working on vtd 8016\n",
      "looking for surrounds, now working on vtd 9018\n",
      "On loop 2 for county 18  we found that vtd frag 9131 now has only 1 neighbor 9130\n",
      "On loop 3 for county 18  we found that vtd frag 9130 now has only 1 neighbor 3130\n",
      "WARNING. unsurrounded vtd 9138 had no found neighbors\n",
      "After surround checks, we appear to have 9024 building blocks defined. We captured 22 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "working on unit neighbor lists for county 20\n",
      "working on unit neighbor lists for county 40\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "if STATE == \"CA\":  #splitting a pesky surrounding border tract; see above\n",
    "    c = countyNo[tractToSplit]  #\"tractToSplit\" ID'd in a previous block\n",
    "    nFrags = len(splitTractGeoms)\n",
    "    v = tractToSplit    \n",
    "    nFragmentedVTDs +=1\n",
    "    fragmentedVTDs.append(v)\n",
    "    geos, areas = [geo for geo in splitTractGeoms], [geo.area for geo in splitTractGeoms]\n",
    "    fragVTDgeom[v] = splitTractGeoms[0]\n",
    "    fragVTDpop[v] = tractPop[v] * areas[0] / np.sum(areas)\n",
    "    for i in range(1,nFrags):\n",
    "        fNo = len(fragVTDgeom)\n",
    "        fragVTDgeom.append(geos[i])\n",
    "        fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "        parentVTDno.append(v)\n",
    "        countyFragVTDset[c].add(fNo )\n",
    "        VTDchildren[v].append(fNo)\n",
    "        nVTDneighbors.append(0)  \n",
    "        lastVTDneighbor.append(-999)   \n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented tracts. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%1000 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[c])\n",
    "                                plotPoly(countyGeom[cc],0.2)\n",
    "                                plotCenter(cc, countyGeom[cc])\n",
    "                                plt.show()\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                if STATE == \"TN\":  #for TN, we tolerate some discontiguous counties\n",
    "                                    print(\"vtd fragment\",v,\"with no neighbors in its county\",countyNo[parentVTDno[v]],\n",
    "                                          \"borders a unit or fused county\",cc)\n",
    "                                else:\n",
    "                                    raise Exception(\"ERROR! Neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "if STATE == \"IL\":  #state-specific manual surround enforcement\n",
    "    specialSurrounded = [20057, 16899, 8757]\n",
    "    specialSurrounder = 21325\n",
    "    print(\"special for\",STATE,\", I believe that\", specialSurrounded,\"are surrounded by vtd (fragment)\",specialSurrounder,\". Here, let me show you\")\n",
    "    c = countyNo[parentVTDno[specialSurrounder]]\n",
    "    plotPoly(countyGeom[c], 0.5)\n",
    "    for v in specialSurrounded:\n",
    "        plotPoly(fragVTDgeom[v])\n",
    "        plotCenter(v,fragVTDgeom[v])\n",
    "    plotPoly(fragVTDgeom[specialSurrounder],0.2)\n",
    "    plotCenter(specialSurrounder, fragVTDgeom[specialSurrounder], 8)\n",
    "    plt.show()\n",
    "    shouldIcombine = int(input(\"enter 1 to apply the surrounding operation\"))\n",
    "    if shouldIcombine == 1:\n",
    "        for v in specialSurrounded:\n",
    "            vv = specialSurrounder\n",
    "            if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                for sNo, s in enumerate(surroundedVTDs):\n",
    "                    if surrounders[sNo] == v:\n",
    "                        surrounders[sNo] = vv\n",
    "            surroundedVTDs.append(v)\n",
    "            surrounders.append(vv)\n",
    "            nVTDneighbors[vv] -=1\n",
    "            nVTDneighbors[v] = 0 #we are capturing all surroundees (some of whom may touch other surroundees)\n",
    "            fragVTDpop[vv] += fragVTDpop[v]\n",
    "            fragVTDpop[v] = 0\n",
    "\n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. unsurrounded vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, this unit appears surrounded.  Next block to confirm it has non-intracounty neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            if STATE != \"WA\" or i * ii != 3 :  #In WA, we don't allow ferry connection of Island to Clallam+Jefferson\n",
    "                unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "                unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "94741893-6835-45ac-9793-64cbaf4823b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Great! all units appear to have multiple neighbors; no checks needed.\n"
     ]
    }
   ],
   "source": [
    "if len(sketchyList) == 0:\n",
    "    print(\"Great! all units appear to have multiple neighbors; no checks needed.\")\n",
    "for L in sketchyList:  #hope that some of these flagged units picked up unit-county or CCB neighbors\n",
    "    print(\"sketchy unit\",L[0], \"has neighbor list\",unitNbrs[L[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should usually be the case, but exception in VA\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "if STATE == \"WA\":\n",
    "    print(\"adding a border unit between Island and Jefferson Counties, as these are only ferry-connected\")\n",
    "    borderUnits.append(2647)\n",
    "    borderType.append(\"vtd\")\n",
    "\n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bc25daa8-4a84-4ceb-8f9a-47735e4cd5fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 255\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "        print(\"above is with unit numbers; below is VTDfrag nos\")\n",
    "        for uu in smallPieceList:\n",
    "            vv = allUnits[uu]\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"v\"+str(vv),unitGeom[uu],8)\n",
    "        v = allUnits[u]\n",
    "        plotCenter(v,unitGeom[u],8)\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "        break\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fc1ae6e1-ef52-412b-a966-e9739071af8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For safekeeping, write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"06Jun24\"\n",
    "print(\"For safekeeping, write the unit topology to a file\")  \n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno, \"allUnits\":allUnits} )\n",
    "outname = STATE+\"tractBasedUnitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d4baaea4-7af4-4407-aeb3-f4291d335f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy()  #optional reset if we need to rerun the below clipPoly routine with alternate clipPolys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "abdd07e7-f9be-480a-9d3d-b3948eeaaa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "toSquishCountiesList = list()   #default; if we did not have any clipPoly's to move in\n",
    "nClips = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4d5fdc3c-6126-4fa8-b484-1d0052c70aed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for many states we move in outcroppings via clipPoly's before running the wedgePoly solver\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state IL\n",
      "clipPoly 0 intersects [1, 34, 43, 63, 75, 76, 90] counties and a total of 69 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "performing squish no 2 for state IL\n",
      "clipPoly 1 intersects [7, 42] counties and a total of 27 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 2 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "#starting with MN, we moved the \"option2\" block earlier in the notebook, as we run it before running option 1 (as in other states)\n",
    "print(\"for many states we move in outcroppings via clipPoly's before running the wedgePoly solver\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(cutLine.buffer(0.02))\n",
    "        plotPoly(altCutLine,0.1)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.002),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "62667c48-6c66-4381-b01d-518af61651bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now apply the squish to all impacted vtds.  \"tractGeom\" will retain original vtd shapes, so we can reset if needed\n",
    "for i, vList in enumerate(toSquishUnitsList):\n",
    "    squishedShapes = squishedShapesList[i]\n",
    "    for j, v in enumerate(vList):\n",
    "        vtdGeom[v] = squishedShapes[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7b967d22-41fb-454f-ae47-d72d712daa4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(nTracts):  #optional full state visualization after squish\n",
    "    if t not in allCutVTDs and t not in skipList:\n",
    "        plotPoly(vtdGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9715564f-3b60-43e5-9f1f-02f3dcee3933",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 22  #pick a county for visualization if desired\n",
    "print(\"here are the faint(original) and squished shapes for county\",c)\n",
    "for t in countyTractList[c]:\n",
    "    plotPoly(vtdGeom[t])\n",
    "    plotPoly(tractGeom[t],0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "760bc48e-0236-4ad7-b3b8-d74795009f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4ef91f8f-dabb-4c58-a8a4-bba8e725e586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use squished shapes (e.g. for MD, MN), enter 0 for unclipped states 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 0 is inside the map's convex hull\n",
      "working on confirming HD center 2003 is inside the map's convex hull\n",
      "working on confirming HD center 4008 is inside the map's convex hull\n",
      "working on confirming HD center 6009 is inside the map's convex hull\n",
      "working on confirming HD center 8016 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S \n",
    "#For GA, MA, MD, MN, NC we draw HDs for ALL vtds, even if they are in a CCB.  We use the clipPoly's to squish in the corners for these HD shapes\n",
    "#Not sure if below would work if we have cut districts AND squished-in corners\n",
    "#We normally draw for each (squished or orig) vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]  #remember, this is after any squish operation\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use squished shapes (e.g. for MD, MN), enter 0 for unclipped states\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[countyTractList[uncutCountyList[0]][0]].buffer(0.1) \n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList and v not in allCutVTDs:\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(0.1))\n",
    "else:  #build the map via simple union of counties not wholly in fully cut-out districts\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if t not in allCutVTDs and t not in skipList:  #keep low-pop tracts as VEST may have assigned voters to them\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%2000 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f00d70b5-482b-4984-b5e2-20abaebc4c19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "special for CA, we will cut some of Nevada out of the convex representation of CA\n"
     ]
    },
    {
     "data": {
      "image/png": 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qsfHodT5YdYiMDMsurISwRbZVhNw4qVx6FFY3hxAqMTwcmGodYymal/fj565VWB15hc/WHsFgxu3ohRD/ZVtFSNPPwDsAZjaD60fVTiNE7tPaAaCxojfr9pUL8l3HQBbuieLb9SekEBHCgthWEeLiBSGblJVyt36rdhohVPBgATvrOBLyQJea/oxqV57p28/xy6bTascRQmSS7a1X75wH/GvD4eWQFKt8L4TNsb6ZYf3qFudeqp4fNp7ExcGO1xuUVDuSEOIFbOtIyAMVXwF9KlzYoXYSIXLZ/VMVGuv81R/SOIAhjUvyzZ8nWLDnotpxhBAvYHtHQgAu7QVHTyjZRO0kQuSu+7NiDFbcI+e9FmW4m6Ln09+O4GJvR6fqMhBdCHNlnR+HXiQ5DlLi4OBS0EvrZ2FDHk7Ntd5ffY1Gw6h25Xmtpj/vrzzI+sNX1Y4khHgG6/1L9DyNRkK59vDHCFjUCW6dVTuRELniYX8QKz0d84BGo+HrVyrRNrAgw5dGsuVEjNqRhBBPYd1/iZ7FOQ90XQA9VykFyMSa8Ps7EHNC7WRCmJgyJsRg5UUIgJ1Ww49dKtOojC+DF0aw++xNtSMJIZ5g/X+JniegGQwNh+aj4ehqmBwEC1+F2EtqJxPCNGysh4a9nZaJ3atSq7gXIfP2EXHxjtqRhBCPsO0iBMDeGeoMg3dPQseZcOMEzGoBh1dCkvzBElbm4cBUO5WD5B5HnR3Te9WgYkFP+s7Zy5ErcWpHEkLcJ0XIAzpHCOwMIf+AV3FYNQC+Lwlz2sD+BTb3CVJYJ1sZE/IkZwc7ZvWtQfF8rvSevZfT1xPUjiSEIIdFyJgxY9BoNLz99tsPb0tOTmbIkCF4e3vj5uZGp06duH79ek5z5h73/NDvT3jnGLQZC/ZOsHYorP9Q7WRCGIFtFiEA7k72zO9fC193R3rMDOPirbtqRxLC5mX7L1F4eDjTpk0jMDDwsdvfeecd1q1bx4oVK9i2bRvR0dF07Ngxx0FznWchqNFfGbzadBTsnQZ3LqidSoicMTwYmGq9fUKeJ4+LAwsGBOHmqKP7jDCiY5PUjiSETctWEZKYmEiPHj2YMWMGefPmfXh7XFwcs2bN4qeffqJJkyZUr16dOXPmsHv3bvbs2WO00LmuZGPlcnkfOLleTs0Ii2Wrp2Me5ePuyKKBQWg00GNmGDEJyWpHEsJmZesv0ZAhQ2jTpg3NmjV77PaIiAjS0tIeu71s2bIUKVKE0NDQp24rJSWF+Pj4x77MToEqEPQGpN6FJa/Bsp6QbIY5hXgBO41ShJy9YdtHAAp4OrMoJIh7qen0mrmXO3dT1Y4khE3KchGydOlS9u/fz7ff/ncV2mvXruHg4ECePHkeu93Pz49r1649dXvffvstnp6eD7/8/f2zGsn0NBpoPUaZzttlPpzfDlPqwqVwtZMJkSXFvJwAWBx+iS/WHSNdn/GCR1ivot6uLAoJ4kZiCr1n7yU+OU3tSELYnCwVIZcuXeKtt95i0aJFODk5GSXAyJEjiYuLe/h16ZIZ9+jQaKB8Bxi0HVzzweIucOOU2qmEyDTN/VOJPYOKMS/0An3m2PZRgABfdxYMqMXFW3cZMDece6myjIMQuSlLRUhERAQxMTFUq1YNnU6HTqdj27ZtjB8/Hp1Oh5+fH6mpqcTGxj72uOvXr5M/f/6nbtPR0REPD4/HvsyeV3Ho9Su4eMOsZnBgsdqJhMicDOVNtlnFQiwcEMTxqwm0n7ST41dt9/RihYKezO1fi6PR8QxaEEFyml7tSELYjCwVIU2bNuXw4cMcOHDg4VeNGjXo0aPHw+v29vZs2rTp4WNOnjxJVFQUwcHBRg+vKue80H8DFG8A696WIyLCMtwvQtDqCC7pzZohdXF3tKfj5N38acMLvVUrkpdZfWqy9/xthi6OJM2GT1MJkZuyVIS4u7tTsWLFx75cXV3x9vamYsWKeHp6MmDAAEaMGMGWLVuIiIigX79+BAcHU7t2bVO9BvW45oOOM8CzMCx4BQ4thzTbHvAnzFzG/U/5Wh0A/l4urHqjDk3L+fLmov2M3XiSjAzbnP0VXNKbqb2qs+1UDCOWH0Rvo/tBiNxk9Hl6P//8M23btqVTp040aNCA/Pnz8+uvvxr7acyHvbNyasavAvw6ECbVgoSnD8IVQnUZ9wdf2tk/vMnZwY4J3aryYauyTNp6hoHz99nsIM3GZXyZ0K0qfx6+yshfD9lsQSZEbtEYDObV9CI+Ph5PT0/i4uIsY3zIo64fhemNodFHUH+E2mmE+K+rB2FaA3h9KxSs+p8fbzkRw/Clkfi6OzK9dw1K+rjlfkYzsDryMiOWH6RPcDFGtSuPxkabuwmRFdl5/7bdjkWm4FcBSjSC03+rnUSIp3tkTMjTNC7ry5ohdQF4eeIutpyIya1kZuWVqoX56uWKzN19gR82nlQ7jhBWS4oQY/MOgNtn1U4hxNM9MSbkaUr4uPHbkLoElfCi/7xwJm89g5kdMM0VPYKK8kmbckzeepZJW86oHUcIqyRFiLEVDYbE67ByAKSnqJ1GiMe94EjIA+5O9kzvVYOhjQP4fsNJhi2JtMkeGiH1S/BOs9L8sPEks3eeVzuOEFZHihBjK9sW2k+AY2tg81dqpxHicZksQgC0Wg3vtijDlB7V2Hwihk5TQrl0+56JA5qf4U0DGNSgBF/8foyle6PUjiOEVZEixNg0GqjWGwK7wNHfIEP6DQgzkoUi5IHWlQrw65t1SExJo8OkXYSevWWicOZJo9HwUeuy9KpdlJGrD7PmwBW1IwlhNaQIMZWidSAuClLi1E4ixL/0WS9CAMrm92DtkHqUK+BOz1lhzN113qbGiWg0Gka3r0DHqoUZsfwgfx2VafhCGIMUIabyYMl06RkizEk2joQ8kNfVgXn9atG3TjE+X3eMD1cdIiXddlqca7UavutUiVYV8jN0cSTbTt1QO5IQFk+KEFMpUFm5lOm6wpw8LELssvVwnZ2WT9uW58fOlfntQDRdp+3henyyEQOaN52dlp+7VqFeqXwMWrCPsHO2dWpKCGOTIsRUClSGInVkcTthXh4UIY90TM2OTtULs2JQMNfikmk3YSf7o+4YIZxlcNBpmdyjGtWK5GXAvH0cuBSrdiQhLJYUIaZy5wJc2QcVXlY7iRD/ykSfkMyq7J+HtcPq4u/lwmvT9rB836Ucb9NSONnbMaN3DUr7udFn9l6bXoVYiJyQIsQUEm/ArBbgkg/qDFM7jRD/ysGYkKfxdXdi8cAgOlUvxAcrD/H52qM2swKtq6OOOf1q4e/lTK9ZYZy9kah2JCEsjhQhpnBqg9KwrPsycHBVO40Q/3qwgJ0me2NCnsZRZ8c3r1Tiy5crsnDPRXrP2svtu6lG274583S2Z37/IPK6ONBjRphN9lERIiekCDGFwyvAoxAUCFQ7iRCPy0gHjRa0xv3V12g09KpdlEUhQZy6nkC7CTs5Gm0b09O9XB1YFBKEo72WHjPDuBZnOwN1hcgpKUJM4fpRcPFWO4UQ/5WhB23OBqU+T1AJb9YOq0deV3s6TdnNuoPRJnsuc+Lr4cSikCDS9Rn0mLmHm4myZIMQmSFFiLEZDErX1GL11U4ixH9lpGd7em5mFcrjzIpBdWhZIT/DlkTy/YYT6DOsv7FZ4bwuLBpYm7ikdHrN2kvcvTS1Iwlh9qQIMbZ7t+DuDXDOq3YSIf4rQ2/U8SDP4uxgx7iuVfj4pbJM3XaWkHnhxCVZ/5ty8XyuLAoJ4mpcEn3m7CUxxfYW/RMiK6QIMTZnL/AOgP3z1E4ixFPcP1KXCzQaDa83KMmcfrWIuHiHVybt4kyM9c8gKZPfnQX9gzgbk8iAueEkpdpOV1khskqKEGPTaqH+uxB3CWJOqJ1GiMcZDEDuFCEPNCztw9qh9bDTanhl0i42Hb+eq8+vhkqFPZnTryaHLscxeGGETbW3FyIrpAgxhTIvgc4J1r9//4++EGZCowVD7r8hFsvnyuohdQku6U3I/H1M3Hza6hfAq1HMi5l9ahB67hZvLTlAuo30TxEiK6QIMQXnPNBsNJzfDue2qp1GiH/ZOYBenbEZbo46pvaszltNSzH2r1MMWbyfu1Y+ZqJuQD4md6/GP8ev8/7KQ2TYwABdIbJCihBTCRoEDu5waqPaSYT4l53u34ZlKtBqNbzdrDTTelVn28kbdJqy2+obfDUr78fPXauw5sAVPllzxOqPAAmRFVKEmIpGA+XaQtgUiFyodhohFFp7MGT8u4aMSlpWyM/qIXVJStPTbuJOdp25qWoeU2tXuSBjOgWyOCyKr/84LoWIEPdJEWJK7SdA0XqwZihER6qdRoh/V89V6ZTMo0r7ubNmSF0qFfKk9+y9zNp53qrfnLvU8Gd0+wrM3Hmen/85rXYcIcyCFCGmZGcPnecABpjeCPTWff5bWIAHRYiKp2QelcfFgTl9azKgXnG+/P0Y7604RHKa9c4k6VOnGB+2Ksv4TaeZuu2s2nGEUJ0UIabm5gv91ivXj69VN4sQdo7KZbr5LDCns9Py8UvlGNe1Cr8fiqbr9D1Wvf7KG41KMqxJAGPWn2BB6AW14wihKilCckPROlC4JoRNUzuJsHV2Dsql3nyKkAderlqIlYPrEBOfTLuJO4m4eFvtSCYzonlpBtQrzqdrjrIy4rLacYRQjRQhuaVaH7i0B5Ji1U4ibJnuQRFingusVSrsydqh9Sjm7cJr0/ewdG+U2pFMQqPR8EmbcnSrVYQPVh7kj0NX1Y4khCqkCMk19wfcpcSrG0PYtgdHQpLj1M3xHD7ujiwKqU3Xmv589OthPv3tCGlW2OhLo9Hw1csVaV+5IG8tjWTzCevvJCvEk6QIyQ23zsK2H6BMG8hTRO00wpZ5B4BTHpjbDnaNh3TzPCLioNPy1cuV+OaVSiwNj6LHzDBuJppn1pyw02oY27kyTcv5MnjhfqufqizEk6QIMbWoMJhQDeKioOXXaqcRts7NF4ZFQGBn+OdzmFgTjq422+UFugcVYcnA2py7cZf2E3Zy5Ir5HsHJLp2dlvHdqlK7hDch8/ax74L1joUR4klShJiaTxnl0jkveBRUN4sQAK75oM2P8GYo+JaDFX1hdku4FK52sqeqUcyLdcPqks/dkVen7mbNgStqRzI6R50d03pWp1JhT/rNCbfKYkuIp5EixNSc80CjjyHpDsxsCjdOqZ1ICIVPGei+DHqvgdR7MKsZrOgHdy6qnew/Cng6s3xQMC9VLMBbSw/w7Z/H0VvZOizODnbM7luTEr5u9JoVxqnrCWpHEsLkNAYza1EYHx+Pp6cncXFxeHh4qB3HeK5EwKoQuHMBGo2Ehh+onUiIf2Xo4eAS2PSlUjDXHgz13wUnT7WTPcZgMDBr53m++fM49Ur5MOG1qni62Ksdy6ji7qXx2ow93ExMYcWgYIrlc1U7khCZkp33bylCclNKIvz9KUTMg3rvQO03wdVb7VRC/Cv1rjJgdfd4sHdWCubqff/ttGomdp6+yZDF+8nrYs+M3jUo5eeudiSjupmYQpdpoaSkZbB8cDCF8jirHUmIF5IixBKkp8CmLyB0kvJHvst8KNHI7P7ICxsXHw2bv4YDiyBfKWj+JZRuqSzMaCaibt1j4Px9XL5zj5+7VqFFhfxqRzKqa3HJdJ62GzuNhuWDgvH1cFI7khDPJUWIJbl5Gua1g4Sr4OmvzJwp30HtVEI87uoh+Ot/cH47FG8ALb6GAoFqp3robko67604yPoj13inWWmGNQlAqzWfQimnLt2+R+epoXg461j6ejBerg5qRxLimbLz/i0DU9WSrxQMj4Seq0CjheW9IWy66kusC/GYAoHQey10WwYJ12BaA/htCMSbR4dPV0cdk7pX493mpfn5n1O8sSiCxBTrWSjS38uFhSFB3EpMpffsMOKTzWPhQSGMRY6EmAN9OqzqD8fWgHcpqPU6VO0JDi5qJxPiX/o0iJgLW7+FtCSo+xbUGQYO5jFw8u9j13ln2QEK5nFiRu8aFPU2j1zGcCw6ntemh1LKz50FA2rh4qBTO5IQ/yGnYyyZwQAXd0PoRDi1AXzKQZ+1Sk8HIcxJchzs+BH2TAEXb2jyCVTuBlo7tZNxJiaBgfMjuH03lYndq1K/lI/akYwmMuoOPWeGUaVIHmb1qYmTvfr7W4hHSRFiLS7vgwWvKAXIa4uVhlJCmJs7F+Cf0XD0V/CrBC2/UgZZqywuKY3hSyLZcfoGI1uXI6R+cTRmNKA2J/acu0XfOXupWzIfU3tVx95OzqgL8yFjQqxF4RrQ+zfISIcl3czm/LsQj8lbDDrPgQH/KDO95neAxV1Vb8jn6WzP7L41eb1BSb7+8zgjlh8kOc06xlrVLuHNtF412H76Bm8vO2B1DduE7ZEixFwVqg69foP0ZJjRRPnUKYQ58q8JA/6CznMh5jhMrg1/vAt31VuMzU6r4aPWZRnfrSrrj1yl89RQomOTVMtjTA1L+zChWzU2HLnGh6sOkSGFiLBgUoSYM++S0O9P0KfAhBpwbK3aiYR4Oo0GKrwCQ8Oh2edwaAWMrwo7x0Fasmqx2lcuyMrBdbh9N5X2E3ey97x1LA7XqmJ+fuxcmVX7L/P5uqOY2Vl1ITJNihBz51UChu1XpvSuGgAb/wf3rOMPqbBCOkeoO1yZfl65G2z+EibVhMMrVVupt2IhT9YOrUtJHze6z9jDwj3mtzZOdrxctRDfvFKJ+aEXGbPhhBQiwiJJEWIJnPMovRpqvQ57Z8C4ShA+y2yXXxcCV2946Xt4cw/4VVQK6FnN4dJeVeJ4uzmyMCSIHkFF+OS3I4z89TCp6RmqZDGmbrWK8Gnb8kzbdo6Jm8+oHUeILJPZMZYm8QZs+Urp1+BeUGmlXelVKFrXrFpqC/GY89uVo3jXDkH5l6H5F5C3qCpRlodf4pPfjhBY2JPJPavh62757dAnbDrNj3+f4pM25QipX0LtOMJGyRRdWxK1R2luduIPiL2o9BVp8SUENJNiRJinjAw4tFRZO+nebagzVFnI0TH3F5+LuHiHNxZGoNVomN67OoGF8+R6BmMyGAx8t+EkU7ed5ZtXKtE9qIjakYQNkiLEFhkMcG4r/DMKrh6EPEVg+EHQypk2YaZSEmHXL8pKvU6e0PQzqNw91//PXo9PZtCCCI5djWdMx0p0rFY4V5/f2AwGA5+vPcr8PRf5uUsVXq5aSO1IwsZInxBbpNFAycbw+jalhXZslNIC3rxqSyH+5egGTf4HQ/dBsXqwZgjMaKR0DM5Ffh5OLH29Nh0qF2TE8oN8+fsx0vWWO05Eo9Ewql0FXq1WmHdXHGTDkWtqRxLihaQIsRYaDbT4Ctr8BEdXw4aRaicS4vny+MOrs6H/X6CxgzmtYXkfuJN7s1ec7O34/tVAPm9Xnrm7L9B3Tjh37qbm2vMbm1arYUynQFpXzM+wJfvZejJG7UhCPJcUIdamRn9o+CGETVHGiwhh7ooEQcgmeGUaXAqDiTWVcSMpibny9BqNhr51i7NgQC2ORsfRftJOTlyLz5XnNgU7rYafu1ahYWkfBi2IYM+5W2pHEuKZpAixNhqNMpUXlDVohLAEWi1Ufk05RVP3LQidBBOqQeQiZUBrLqhTMh9rh9bDzdGejpN3s/6w5S6XYG+nZWL3atQolpcBc8OJjLqjdiQhnkqKEGv0oJnZqQ3KJ8ot30DcFXUzCZEZ/xkv8ibMaAwXQ3Pl6f29XFj1RjCNy/ryxqL9/PTXSYtti+5kb8eM3jUoV8CDPrP3cizaco/uCOsls2OsVdh0CJ2oXI+NUjqv9lihtIIXwlJEhcGGjyB6v9IWvtnoXOkvYjAYmLLtLD9sPEnTsr783LUK7k72Jn9eU4hPTqPHjDCiY5NYNiiYAF83tSMJKyVTdMXT7Zmi/CHPXwkG71Q7jRBZk5EBh5bBP59D0p37/UVGKEdNTGzLiRiGL43Ez8OJ6b2qU8LHMt/A79xN5bXpe4hNSmXFoDoU8XZRO5KwQjJFVzxdzDHl0tFT3RxCZIdWC1W6wbAIZV2aXBwv0risL78NqYvBYKDDpF1ssdDZJnldHVgQUgtnezt6zNrD1TjrWFFYWD4pQmxBk0+V0zFXD6i6oqkQOeLoBk0+UVbqzcXxIiV93Fg9pC61innRf244U7aetcjF4nzdnVg0sDYZGdBjZhg3ElLUjiSEFCE2wc0Xmo6C1ERlVVMhLFmeIvf7i2xUZoPNaQUr+pq0v4iHkz0zetdgSKMAvttwguFLD5CUqjfZ85lKoTzOLAoJIiE5nV6zwoi9Z7k9UYR1kCLEVlR4GbxK/jtzRghLV6Q2hGyGl6cqR0NM3F9Eq9XwXssyTO5RjX+OXafTlN1cvnPPJM9lSsXyubIoJIjr8cn0mRNOQnKa2pGEDctSETJlyhQCAwPx8PDAw8OD4OBg1q9f//Dn165do1evXuTPnx9XV1eqVavGqlWrjB5aZMOVCLh9Fsq0VjuJEMajwniRlyoV4Nc365CQkkb7ibsIPWt5zcBK+7mzYEAQ52ISGTBvn0Ue1RHWIUtFSOHChRkzZgwRERHs27ePJk2a0KFDB44ePQpA7969OXnyJGvXruXw4cN07NiRLl26EBkZaZLwIpMMBvh7FOQrA2XbqJ1GCOPL5fEi5Qp4sHZIPcrmd6fnrDDm7b5gceNEKhbyZG7/mhy5EseghRGkpEshInJfjqfoenl58cMPPzBgwADc3NyYMmUKvXr1evhzb29vvvvuO0JCQjK1PZmiawJHf4MVfaDHKghoqpxHF8KaRe25318kEip2guZfgKfxV8lN12fwzZ8nmL3rPF1qFObLlyviqLMz+vOY0u4zN+k7N5xGpX2Y1KMa9nZyll5kT65O0dXr9SxdupS7d+8SHBwMQJ06dVi2bBm3b98mIyODpUuXkpycTKNGjbL7NCKnkuNh4/8gb3HYMRa+8oNv/eGXyrC4KxxaITNmhPV5MF6kw2Q4v0MZL7Lte0gz7tRUnZ2Wz9qV58fOlfntQDSvTd9DTLxl/T7VCcjH1J7V2HwihvdWHERvoR1ihWXK8pGQw4cPExwcTHJyMm5ubixevJiXXnoJgNjYWLp27cpff/2FTqfDxcWFFStW0KJFi2duLyUlhZSUf6eKxcfH4+/vL0dCjGVmc7i899/vq/WGfKUhMQYuh0NUKLgXUFbfLdNajpII65McD9t/UJr2eRRQVpsu197o/9cPXIpl8III9AYDP3auTIPSPkbdvqn9cegqw5bsp2tNf755pRIa+VsgsihXjoSUKVOGAwcOEBYWxhtvvEGfPn04dkxphvXpp58SGxvLP//8w759+xgxYgRdunTh8OHDz9zet99+i6en58Mvf3//rEYSz1OxE/gHgbMXOOWBmgOhzjBo8SX03wBDwiFfKVjaDXZPUDutEMbn5KH8f39zD/iUg+W9YV47uH7UqE9TxT8Pa4fVpVwBD3rP3suXvx+zqHEWbQIL8P2rlVmy9xJf/n7c4sa4CMuU4zEhzZo1o2TJknzwwQcEBARw5MgRKlSo8NjPAwICmDp16lMfL0dCzIDBAH++B+EzoVJnqP0GFKqudiohTOP038p4kdvnoEZ/aPw/cPEy2uYzMgzM2X2B79afoISPKxO6VaWUn7vRtm9qC0Iv8OmaowxrEsC7LcqoHUdYkOwcCdHl9EkzMjJISUnh3j1lvrxW+/jBFTs7OzKeM1XO0dERR0fHnMYQOaHRKIeoPQrB/vkwqyW0GQv6NIi7BAWrKYevtTJgTViBUs2heEPYO00ZJ3JklVKIVO8Hdjn+k4hWq2FAveIEl/DmraWRtJ2wk0/alKNn7aIWcYqjV3Ax7qXq+Xb9CZwd7HizUYDakYQVy9KRkJEjR9K6dWuKFClCQkICixcv5rvvvmPjxo00atSI8uXLU6BAAcaOHYu3tze//fYb77//Pr///vvDcSMvIrNjVJZ6F5a8Bue3g0arzCiIjYLCtaB6X6jcTYoRYT0SY5QGZ5ELwbc8tB4DxRsYbfPJaXq++fM480Mv0qycL991CsTbzTI+dP309ynGbzrN6PYV6FOnmNpxhAUw+Sq6AwYMYNOmTVy9ehVPT08CAwP58MMPad68OQCnT5/mo48+YufOnSQmJhIQEMB777332JRdU7wIYQK3z4Gdg1KEnP4Htn2nDHD19FdO19R+UwaxCusRHQnrP4RLYcpRvxZfQd6iRtv8puPXeX/lIbQaDT92qUxDCxi0ajAY+ObP48zYcZ7vXw2kSw0Zryeez+RFSG6QIsSMRYVB6AQ4vg5KNlH6jshREWEtDAY4vAL+/gyS7kDdt6HuW+BgnGXvYxKSeW/FIbafukH/usX5oFUZnOzNu6eIwWDgk9+OsGRvFL+8VpV2lQuqHUmYMSlCRO6IXAhrhsAr06Dya2qnEcK4UhJhx48QOhHc/KDl10ab0puRYWDu7guMuT9odXy3qpQ280GrGRkG3ltxkLUHo5naszrNyvupHUmYqVxtViZsWNWeULoV7J2hdhIhjM/RDZqNUqb0+lVQpvTO7wAxx3O8aa1WQ/96xVkztC76DAPtJuxkfqh5t3zXajV8/2ogzcr58eai/ew8fVPtSMKKSBEisqdEY7h2GPTpaicRwjS8S0L3ZdB9BcRdhil1Yf1HkBSb402XK+DBumH1eK2mP5+tOUrIvH3cTEx58QNVorPTMr5bVeoEeDNw/j7CL8hq3MI4pAgR2VOgMuhT4NohtZMIYVqlW8CbodD0U2UK+4TqymUOV+l1srdjdIeKzO5bgwOXYmk1bgdbT8YYKbTxOei0TO1Zncr+nvSfE86hy7FqRxJWQIoQkT2Fa4KjJ5zdpHYSIUxP5wj13oFhEcoikGuHwcwmcHlfjjfdpKwf69+uT4WCHvSdE87odUdJTjPPTqtO9nbM7FOTAD83es/ey8lrCWpHEhZOihCRPXY68CkDMSfUTiJE7vEoAB2nQ/+NkJEOM5vCb29CwvUcbdbX3Yk5fWsyql15FoVF8fKkXWb7Bu/mqGNu31oU9HSmx8wwzt+8q3YkYcGkCBHZlxJvtOmLQliUIrXh9W3Q9mc4uV45RbN7otJlOJu0Wg396hZn7dC6ZBgMtJ+4k3m7zXPQqqeLPQsG1CKPiz09Zuzh8p17akcSFkqKEJE9m76AGyfA1VftJEKoQ2unrD0zLEKZqv73pzClDpzdnKPNls3vwdqhyqDVUWuP0n9uuFkOWvV2c2ThgCB0dlp6zAwjJj5Z7UjCAkkRIrLnwk7QOUOdoWonEUJdLl7KWkuDtoOrDyx4BZb2gDsXsr3JB4NW5/StyeErcbQat50tZjhoNb+nE4tCgkhNz6DHzDBumWGxJMybFCEie3ROkL8SOOdVO4kQ5iF/Jej7B3SaBVf2w8RasPlrSM3+qYrGZX1Z/1YDKhbypN+ccD5fa36DVv29XFgYEsSde6n0nr2XuKTsn5IStkeKEJE9OkeltXXCdeVSCKF0Va30KgzbB3WGwa5xMKkWHFujtIXPBh93R+b0rcnn7cqzeG8UHSaa36DVkj5uLBgQxOU7SfSbs5e7KdI/SGSOFCEiexp+CLdOw4+l4afycHG32omEMB8OrkpfkSFh4FdR6bq64GW4cTJbm9NoNPS9P2gVoN3Enczddd6sBq2WK+DB/P61OHU9kZB5+8zuiI0wT7J2jMi+yxFwNRL2zQHXfNB7jdqJhDBPpzYqq/TGXYKgwUoR75S9v2/JaXrGrD/B3N0XaFzGh+9frYyPu6ORA2ff3vO36T07jDol8zG1Z3UcdPJZ11bI2jEidxWuDjVDoGwbiD4AqdIvQIinKt1SWYum0UgInwUTa8DBZdk6ReNkb8fn7Sswp58yaLX1L9vZcsJ8Bq3WKu7F9F412Hn6Ju8sO0C6PmedZYV1kyJE5FxgV0hLgk1fqp1ECPNl7wQN3oOh4VAkGFa/DnNaw9XsLX3QuIwyaLVSIU/6zTWvQasNSvswsXtVNhy9xoerDpORYVYH3IUZkSJE5Jx3Sag/AsKmwtktaqcRwrzl8Ycu86D3WmVQ9/SG8PsIuJf1ReF83B2Z3bcmo9tXeDho9cS1eBOEzroWFfLzU5fK/Bp5mc/WHjGr8SvCfEgRIoyjwQeQtxjs+kXtJEJYhhINYfBOaPEVHF4BE6pB+EzIyNrRDI1GQ586xVg3tB4A7SfuYo6ZDFrtUKUQYzpWYuGeKL5df8IsMgnzIkWIMA6tFrwDlKm7QojMsbOH4CFK19UybeCPd5UjI1F7srypMvndWTO0Lt1rFWH0umP0mxvOjQT1m4d1rVmEz9qWZ/r2c4zfdEbtOMLMSBEijOfGCfApq3YKISyPmy+8PAlCNoNWB7NbwurBkJi1AaePDlo9ciWeVuO2s/lEzhbXM4b+9Yrzfssy/PzPKWZsP6d2HGFGpAgRxhF7SZl+6FdB7SRCWK7C1SFkE7QdB6c2KAvj7ZkK+qw1/2pcxpcNb9cnsLAn/efuY9SaI6oPWh3SOIAhjUvy9Z/HWbjnoqpZhPmQIkQYx6Uw5bJIsLo5hLB0Wjuo0Q+G7YeKnWDDRzCtAVzYlaXN5HNTBq1+0aECS8Mv0X7iTtUHrb7Xogx96xTj0zVH+HX/ZVWzCPMgRYgwjuINwM4Bjq5WO4kQ1sHFC9qNg4Gblem9c1+CVQMh4VqmN6HRaOgdXIx1w+qh1WhoP3EXs3eqN2hVo9HwWdvydKnuz3srDrL+8FVVcgjzIUWIMA43XyjfAf4ZBYu7wt2baicSwjoUqgYD/oH2E+DsJphQA3ZPBH3mF4or7efOb0Pq0iOoCF/8foy+c8KJSUg2Yehn02o1fNOxEm0CCzJ8aaRZNVoTuU/atgvjSY5Tzl9v+w68SkD/jeDqrXYqIazHvduw5Wul66pvOWjzExTN2inQrSdjeG/FIQwGA9+/GkjTcn4mCvt8afoM3li4nx2nbzCnX03qlMynSg5hPNK2XajLyRMafQhd5iuL2/1QQllXJgdLmQshHuHiBW1+hNe3gr0zzGkFv72ZpSOPje4PWq3in4cB8/bxmUqDVu3ttEzsXpVaxb0ImbePiIuyGrctkiMhwjQ+91QuNVpw8YZev0H+iqpGEsKqZGTA/nnwz+fK981GQbU+ysDWTDAYDCzYc5Gv/zhOES8XxnerSrkCuf83915qOn1m7+XEtQSWDKxNxUKeuZ5BGEd23r+lCBGmkZ4K6cmQdBuW9YLE60oh4lde7WRCWJe7N+HvUXBgIRSqrhwpKVg10w8/dT2B4UsiOXfjLh+2Lku/OsXQajUmDPxfCclp9JgZxuU7SSwfVJsAX/dcfX5hHHI6RpgPnYOyVHneYtDzV3DJBzOawOl/1E4mhHVxzac0Ouu/EdKSld+zP96DpNhMPfzBoNVewUX58vdj9J2b+4NW3Z3smd+/Fr7ujnSfEcbFW7Iit62QIyEid6TegyWvwfltyqq7DT9UFr4TQhiPPg32Toct3yhjRlp8DYFdQJO5IxvbTt3g3eUHyTAY+EGFQas3ElLoOi2UlPQMVgwOpmAe51x9fpEzcjpGmDeDASLmwLbvlXbUVXtAtb5Kl8hH75OSAI7ucCUCIheCvQvUHQ7u+VWLLoRFiY+GjR8rfXuK1lNO0fhmbkmFW4kpfLDyEJtOxNCrdlE+fqkczg6ZG2diDNGxSXSeGoqjTsuyQcH4uMt6VJZCihBhGdKSIHSSMnMm/rKy8J1vObB3hYu7IS5KKTzS7oGzlzKuROcEzT6H2m+onV4Iy3FmE/z5PsRehOCh0PADcHB94cMMBgMLw6L46vdj+Hu5MP61qpQvmHt/jy/eukvnqaHkdXFg6eu1yevqkGvPLbJPihBhWTL0cGojnNsCN04qh5I9CipHPBw9lHPd1fsq/Ue2fqscZm41RgoRIbIiPQV2jYcdY5WxWa3HQNm2mTpFc/p6AsOXHuBsTCIftCpD/7rFc23Q6unrCXSdvofCeZ1ZFBKEu5N9rjyvyD4pQoT1Mhjg709h9wToPA8qvKx2IiEsy+3zsP4DOP0XlGoBrb8Hr+IvfFhKup7vN5xk1s7z1C+Vjx87V8bXwykXAsORK3F0m7GHsvndmde/Fi4Oulx5XpE9MjtGWC+NBpp/CRU6Ks2Zrh1RO5EQlsWrOHRfDl0XwfVjMLm2Mj4rPeW5D3PU2fFp2/LM71+LE9cSaPXLDv45dj1XIlcs5MncfrU4Gh3PoAURpKSruxKwMD4pQoTl0Gigw0TwLgFLukGyuiuCCmFxNBoo1xaG7oWgwcoSC5ODlbEjL9CgtA8b3qpPtSJ5CJm/j09+O0xSqumLgupF8zKrT032nr/N0MWRpOkzTP6cIvdIESIsi4Or8knu3k3YPV7tNEJYJgdXaD4aBu8C9wKwsCOs6KvMqnkObzdHZvSuwZcvV2TFvsu0m7iTo9FxJo8bXNKbqT2rs/VkDO8uP4g+w6xGEYgckCJEWB6PQsrA1ZREtZMIYdl8y0Lf3+GV6XBhJ0ys+cIVejUaDb1qF+WP4fWwt9PyyqTdzNxxjgwTFwaNy/oy/rWq/H4omo9/PWzy5xO5Q4oQYXku7oLEaxDYWe0kQlg+jQYqd4Wh+6BKd2UA+LSGELXnuQ8L8HXntyF16FOnKF/9cZw+c/YSE2/aTqutKxVgbOfKLNt3iS9+P4aZzasQ2SBFiLA8N0+BVgcFMr8+hhDiBZzzwEs/wMDNoHOE2S3htyHPXaHXUWfH/9qUZ8GAWpy8lkDLcdv528SDVjtWK8xXL1dk7u4LjP3rpEmfS5ieFCHC8lw/ojQ408p/XyGMrmBVCPkH2v4MJ36HCdWVxoIZzx4QWr+UDxvebkD1ol4MnL+P/6027aDVnrWL8r+XyjFpy1kmbTljsucRpid/xYVlSboDR1ZDqeZqJxHCemntoEZ/5RRN2Tbw+9swqxlEH3jmQ7xcHZjRuzpfv1KRVftNP2h1YIMSvN2sFD9sPMmcXedN9jzCtKQIEeYr8QYs6gxrh0PCNeWT2B/vAgaoPUTtdEJYPzcfeHky9NugLLcwo7HSBv4ZK/RqNBp6BBXl92H1cLDT8vKkXczYbrpBq281LcWgBiUYve4Yy8KjTPIcwrSkY6owT/o0+L4kpNz/JKVzAq8SEHMcXp0NFTuqm08IW6NPg7BpyhIK9i7Q8muo1PmZ7d9T0vWM3XiSGTuUTqtjO1fGzwSdVg0GA5+uOcKisCjGda1ChyqFjP4cInOkY6qwHr8OVAqQduPhvdPKoWFnL+j1qxQgQqjBzh7qDIUhe6FoHeV3dF47Zd2np3gwaHXhgCBOXkug1bjt/HX0mtFjaTQavmhfkVeqFmLE8oMmeQ5hOnIkRJinpT2UQXEAff+AYvXUzSOEeNyZf+6v0BsFdYZBg/efuULv7bupfLjqEH8fu073oCJ82qY8zg52Ro2Trs9g+NJI/jkWw8w+NWhQ2seo2xcvJkdChPXoPA88iyjXDyxWN4sQ4r8CmsEbodDgAwidDJOC4PjvymKTT/BydWB6r+p880olft1/mTYTdnDkinEHrerstIzrWpW6Ad68vmAfe8/fNur2hWlIESLMk50O4i4p18t3UDeLEOLp7J2g0YcwZA/4lIVlPWBxV2XF3idoNBq6BxXh92H1cba345XJu5i+/axRB6066LRM6Vmdqv556T83nIOXYo22bWEaUoQI89VopHLpKKflhDBrXiWgxwrouhCuH72/Qu8PT12hN8DXjV/frEO/usX55s8T9J69l+tG7LTqZG/HzD41KO3nRu/Zezl+VRa6NGdShAjzVfctQKOcdxZCmDeNBsq1gyFhEDQIto2BKXXg3Lb/3NVRZ8fHL5Vj4YAgTsconVY3GnFAqaujjjn9alE4rzO9ZoVx7oasM2WupAgR5sveCeqPgOuH4c4FtdMIITLD0Q2afwGDd4KrL8xvD7++rvT9eUK9UvnY8FYDahXzYtCCCEb+eph7qelGieHpbM+CAUHkdXGgx8wwLt2+Z5TtCuOSIkSYt0pdlMt/Plc1hhAii3zLKTPb2k+E03/BxBoQMfc/7d/zujow7f6g1dWRl2k7YafRBq16uTqwMCQIB52WHjPDjHraRxiHFCHCvPmWVS6PrlY3hxAi67RaqNYLhkYo7d/XvQVzWinjRh7x6KBVFwdl0Oq0bcYZtOrn4cSikCDS9Rn0mBnGrcT/jlMR6pEiRJg/j8JqJxBC5ISrt9L+ve8fyvpP0xrA359B6t3H7hbg68avb9Slf73ijNlwgl6zw7gWl/OjF4XzurAwJIjYe2n0mrWXuHtpOd6mMA4pQoT5q/Cycnl45VN7EAghLESxespYkYYfwZ6pMKk2nNr42F0cdFpGti7HogFBnIlJpNUv29lwJOeDVkv4uLEwpBbRcUn0nbuXxBTjjD0ROSNFiDB/lV6FgOawagDMaa18khJCWCadIzR8H94MhXwBsLgLLOsJ8dGP3a1OgDJoNai4F4MXRjDy10M5HrRaNr8H8/vX4sz1RELmhZOcps/R9kTOSdt2YRkMBjjxh9IMCaD5l1D5NXDzVTeXECL7DAY4sgo2jFRW6W36KdQMAa3dI3cxsCz8EqPXHaNAHifGv1aVioU8c/S04Rdu03vWXoJKeDG9Vw0cdPJ53Biy8/4tRYiwLPvmwKYvIOk2aHVQuhVU7akcKbHTqZ1OCJEdSbHKDLiIOVCoOrQdBwUCH7vL2RuJvLU0kpPXEni3RRler18CrfbpK/hmxs7TN+k/N5ym5XyZ0K0qOjspRHJKihBhGwwGSLwOx9dB5AK4ehB8K8BrC5XOjUIIyxQVpsyguXkKgt9UuiY/siheanoGP/59kunbzxFcwpufulQhv6dTtp/un2PXGbwwgvaVCzK2c+UcFTVCihBhqy7vg5X9Ifai0lekwyTQOaidSgiRHempEDoBtn2vNDtr8yOUbvHYXXafucmI5QdJTtczpmMgrSrmz/bTrTsYzfClkXSvVYSvXq6IRiOFSHbJKrrCNhWuAW/shsK14PBy+KEkJBt3hU4hRC7ROUD9d5WBq94lYXFnWN4HEv6dIVMnIB/r36pP7eLeDF4YwUersj9otV3lgnzXMZBFYVF8/cdxzOxzudXLUhEyZcoUAgMD8fDwwMPDg+DgYNavX//YfUJDQ2nSpAmurq54eHjQoEEDkpKSjBpaiP9wdIOQv5VlxVPiYfFrMp1XCEvmVQJ6rYaOM+DCTphYC8JnPey4mtfVgSk9qzGmYyXWHIim7fidHL6cvQ8fXWr683m78szceZ5x/5w25qsQL5ClIqRw4cKMGTOGiIgI9u3bR5MmTejQoQNHjyrd70JDQ2nVqhUtWrRg7969hIeHM3ToULRaOeAickmT/8FrSyBqN6wZAmnSplkIi6XRQGAXGBoOFTrAHyNgdku4fuz+jzW8VqsIfwyvh6ujjo5TdjE1m51W+9YtzgetyvDLptNM23bW2K9EPEOOx4R4eXnxww8/MGDAAGrXrk3z5s358ssvs709GRMicsxggBV94NgaqNAROs9RO5EQwhgu7ILf34bb56DOMOXIp4MLoAxa/envU0zbfpbaxb35qWtlCng6Z/kpfvzrJBM2n+HLDhXoFVzMuPmtXK6OCdHr9SxdupS7d+8SHBxMTEwMYWFh+Pr6UqdOHfz8/GjYsCE7d+7M7lMIkT0aDXSZr1w/+ivMaAqrB8O1w+rmEkLkTLG69zuufgihk2BKMJzZBCidVj9qXZZFIUGcv3mXVuN2sP7w1Sw/xYjmpelftzifrjnKyojLxn4F4glZLkIOHz6Mm5sbjo6ODB48mNWrV1O+fHnOnTsHwOeff87AgQPZsGED1apVo2nTppw+/exzbCkpKcTHxz/2JYRRvH8WqvWBK/vg4BJlvYpTf6mdSgiREzpHaPgBvBEKnv6wsCOsCoHEGADqlMzHhrfrU6ekN28s2s+HKw9xNwst2jUaDZ+2LcdrNf35YOVB/sxGISMyL8unY1JTU4mKiiIuLo6VK1cyc+ZMtm3bRmxsLHXr1mXkyJF88803D+8fGBhImzZt+Pbbb5+6vc8//5zRo0f/53Y5HSOMSp8G89pDcqwyk0am4Qlh+QwG5QPGxv+BIQOafwFVe4FWi8FgYPm+S3y+9hj5PZ345bUqBBbOk+lN6zMMjFh+gD8OXWV67+o0KetnutdhJXLldIyDgwMBAQFUr16db7/9lsqVK/PLL79QoEABAMqXL//Y/cuVK0dUVNQztzdy5Eji4uIefl26dCmrkYR4MTt7qNIdYo7B5q/UTiOEMAaNRvm9HroPyrwE64bD3DYQcwKNRkPXmsqgVXcnHR0n72by1jPoMzlo1U6rYWznyjQp68vghfvZfeamiV+MbcrxtJWMjAxSUlIoVqwYBQsW5OTJk4/9/NSpUxQtWvSZj3d0dHw45ffBlxAmUbmbcrljrLo5hBDG5eoNr0yBPuuUbspT6ykfNtKSKeHjxsrBdRjYoAQ/bDxJj5l7uBqXubYR9nZaJnSvSlBxL0Lm7yPi4m0TvxDbk6UiZOTIkWzfvp0LFy5w+PBhRo4cydatW+nRowcajYb333+f8ePHs3LlSs6cOcOnn37KiRMnGDBggKnyC5F5dvfXmgGl+VFKorp5hBDGVbyBcrq13juwcxxMrQsXduKg0/Jhq7IsDqnNhZv3sjRo1VFnx/ReNahYyJO+c8I5ckUaIRpTlsaEDBgwgE2bNnH16lU8PT0JDAzkww8/pHnz5g/vM2bMGCZNmsTt27epXLky33//PfXq1ct0IJmiK0wqKRam1YfYKHD0gO7LoWiw2qmEEMYWc0I5PXMpDKr1VsaLOOcl9l4qH68+zJ+Hr9GlRmFGtauAq+OLF79MTEmnx8wwom7dZdmgYEr7uefCi7AssnaMEJkVGwWr34DoSGj4PgQNBvus9xQQQpixjAyImA3/jAadE7z0PZR/GQOwIuIyn689ip+HE+O6VqGyf54Xbi72XiqvTd/D7bupLB8UTLF8ri98jC2RtWOEyKw8RaDHcqjcFTZ9AfPawd1baqcSQhiTVgs1Q2BIGPjXghV9YclraOKv0KWGP38Mr4+7k45OU3YzacuLB63mcXFgwYAg3Jx09JgZxpVYWZIkp+RIiBBXImDBK+BTDgZsVDuNEMJUjq+DP96D1ERo+hnUDCHNoOHnv08xZdtZahXz4ueuVSiY5/lHRa/GJdF5aij2dlqWDaqNr7tTLr0A8yZHQoTIjkLVlVU7L+2BW7JmhBBWq1w7GLoXArvC+g9gVgvsbx7ng1ZlWTKwNlG379H6lx0vbFBWwNOZxSG1uZeaTs+ZYdy5m5pLL8D6SBEiBEDFTspl5EJ1cwghTMvJE9r+BP03QkqC0kl505fU9ndlw1sNqBeQjzcX7ef9FQef22m1iLcLi0Jqcysxld6z9xKfnJaLL8J6SBEiBIBHIajSA3b+BLsnqJ1GCGFqRWrD4B3Q4H3YPR6m1MHz+h4mdq/K968G8sfhq7QZv4ODl2KfuYkAXzcWDAji4q279J8Tzr3UzLeHFwoZEyLEAwYD/FgWEq+BgxuUbAJF64JnIeXSxUvthEIIU7hxEta9BVGhStv3Fl9y4a4Dby2N5Gh0PO80L83ghiWx0z59uYfIqDv0nBlG1SJ5mdmnBk72drn8AsyDTNEVIqdiL8HBpXDrNMRHw4Udyu32rtB9qdIMSQhhfTIyYP9c+HuUMp239RjSyr7MuE2nmbz1xYNW95y7RZ/Ze6lfKh9TelbH3s72TjRIESKEsd0+D7fPwvYfldV4O0yCwC5qpxJCmEr8VVj/vjKTplRLaPMje267MGLZARJT0vm2YyBtAgs89aFbT8YwcP4+WlbIzy+vVX3mkRNrJbNjhDA2r+IQ0Ax6rgI3P/h1IBxYonYqIYSpeBSArguh6yK4dhgm16Z2zArWD6tL/dI+DFmsDFpNfMqg1UZlfJnQrRrrj1zjo1WHyMjkYnm2TIoQITLDwQUG/AV2DrD+Qzi7Re1EQghTKtdWaXJW+TXY8BGeS15iYlNHxnauzJ/3B60eeMqg1VYV8zO2cyAr919m9LqjmNnJBrMjRYgQmeVREEacUAaqLuwEoZOV88hCCOvk5AFtfrw/nTcRzbSGvBq/gD+GBJHHxYFOU3YzcfPp/3RafaVqYb5+uRLzQi/y3YaTUog8hxQhQmSFqze8vhWCBsHGkbDoVUiMUTuVEMKUigQp03nrvQM7xlJsZWtWtbPnjYYl+fHvU3Sbsec/Ldy7BxXhkzblmLrtLJO2nFEpuPmTgalCZNfpf+C3NyA5DvIWg6J1oHBNSLoN5V+GPP5qJxRCGNu1I7B2KEQfgNpvEl78Dd5efZqE5DS+6ViJtoEFH7v7hE2n+fHvU3zatjwD6hVXJ3MukdkxQuS22EsQuQDu3lBG09+9odzu4A591igt4YUQ1kWfDnsmw5avwc2PxJY/81FkXn4/dJVO1QozukMF3Bx1ABgMBr7bcJKp287ybcdKdKtVROXwpiNFiBBqysiAjDRISVRO0yTHwrD9oLGtaXpC2IxbZ2HtcLi4E0PV3qz1e4OP/4win7sj47pWoWqRvIBSiIxae5QFey4yrmsVOlQppHJw05ApukKoSasFnaMybqRqT7h9Dq4fUTuVEMJUvEtCn3XQdhyaY7/RYecrbGmbSF4XB16dGvpw0KpGo+HzdhXoVK0wI5YfZOPRa2onNxtShAhhCs7KJyAZtCqEldNqoUY/eHMPFKyC758D+DXfdN6rk4ef/j5Ft+l7uHznHlqthu86BdKqYn6GLY5k26kbaic3C1KECGEKdy4oPUX8a6mdRAiRGzwLQbel0GkW2os7eONod/5uepUrd+7R+pcdrD0YjZ1Ww89dqlC/VD5en7+PPeduqZ1adVKECGFssZdg58/KqryO7mqnEULkFo0GKr0KQ8IhoBkld77L1oKTeKVEBsOXRDJi+QFS9RlM6lGN6kXzMmBu+FMbntkSKUKEMLZtYyAlHpp+pnYSIYQaXL2h00zotgz7m8cZfTmE1TWO8teRaF76ZQfHrsYzo3cNyhbwoPesMI5Fx6udWDVShAhhbPr7a0rsmQzmNflMCJGbyrSCIWFoArtQ9cjX7Cv0E5WcYug8NZRZO88zq08Nini70GtWGGdiEtVOqwqZoiuEsRkMsCoEjqyEwrWgel8I7Ap2OrWTCSHUcmEnrB2GIe4KOwsNYMDp2lQu6sOodhUYsfwA8UnprBgcjL+Xi9pJs036hAhhLjIy4Nhq2DsDokKV26r2BN/ycOYfyNArhUnFjmDvrG5WIUTuSEuCrd/C7gnczVuWoYn92ZdahLealmLhnovoDQZWDKpDfk8ntZNmixQhQpgbgwEi5sL1o3B4hdLALG8xyFMUzm8DnRPUfRsaj1Q3pxAi91zZrxwViTnOP3m7MjS6BVWK5+f41Xh83B1ZNiiYfG6OaqfMMilChDBn+jRl6q5XSaW3QMxxiFwIoRNhwD/gX1PthEKI3KJPg13jMGz7nkSnggxJ7MeO1FIYDFA2vztLX69NHhcHtVNmiXRMFcKc2dlDvlJKAQLgWw6afwn2LnB5r7rZhBC5y84eGryPZvBO3PP6Ml8zisl5FuHGPU5cS6DPnHASU9LVTmlyUoQIoSatFpw8lZV4hRC2x6cM9N8Arb+nVfpWdnv8j8Z2Bzh4KZb+c8NJStWrndCkpAgRQk3pqUprdzc/tZMIIdSitYOgQWjeDMWjcHnm2H/PVJdpnD5/gcELI0hJt95CRIoQIdR0YQcY9FBYxoMIYfPyFoVeq+HlKbR0OMh214+4e3oHI389rHYyk5EiRAg1HV4JaCB/JbWTCCHMgUYDVbqjGbIXd0cdP5Y6QkFP653GL0WIEGpJjoeDi6H2G8ofHiGEeMDdDzBQtFBB3mtZRu00JiNFiBBqCZ2kXFbtqW4OIYR5csqjTOW1YlKECKGWuzeUyyl1IeGaulmEEOYlIwPu3gRHN7WTmJQUIUKopcWX0H4CYICfK8Ltc2onEkKYDYMyaP1yONw8o3YYk5EiRAi1OLhCtd4wPBIy0uDMJrUTCSHMhdYOOs5QCpBJNeHAYrUTmYQUIUKoLfaSclmouro5hBDmpexLyoeUCh1h48fKAnhWRooQIdQWPgO8A6BAFbWTCCHMjb0TNPlE6aq86UtlrIgVkSJECDXFnIDj65QBaFr5dRRCPIVXcWj5DeyZBLOaKavwWgn5qyeEmrxKgEs+SI6FE3+qnUYIYa5qvwH9NkB6CsxoDAs7wa2zaqfKMSlChFCTzgHePwP2rrB7vNpphBDmrGgwvL4N2vwIF3bBurfUTpRjUoQIobY75yE9CSp1VjuJEMLc2emgZgi0/VlZe+rCLrUT5YgUIUKoLXQSOHtBle5qJxFCWIqSjZXLFX3UzZFDUoQIoaZLeyF8JtR6Heytd5EqIYSRueeHdr8onZdDJ6udJtukCBFCTVu/VS5r9FM3hxDC8hSqoVxuHAmJN9TNkk1ShAihJt/yyuXYUvCFN8xqCYeWQ+pddXMJIcxf/opKV1UXb1jaHfTpaifKMp3aAYSwaS2+AvcCcP0IpCRASjz8OhDsXZQpeU0/UzuhEMKcBXaBvMVgdisImwp1hqqdKEukCBFCTRrNf/9o3D6vjBPZ8aPSR6RqT3WyCSEsg38t8CsP1w6rnSTLpAgRwtx4FVeOkNy7DWuGKKds8haDGycg6Q4kxSqD0WKOK62c0+4pa0qkJSlTfdOSlNvQKEVM4ZpKP5K0JGXwq1cJZTqwg6vKL1QIYRQGA9w6B8UbQuo95fdco1E7VaZoDAaDQe0Qj4qPj8fT05O4uDg8PDzUjiOEevRp8GW+p//MwR18yoCrj/IHx95FWWPi4XVnyNBDzDG4ekhZpdfeBRKuKYWMzhEKVoN8AVCsAZRpBY7umcuVkaF0eHXOazF/6ISwamnJ8HN5uHdL+b5AZWj8CZRqnqu/o9l5/5YiRAhzdmEnnNsGLl5QrJ4yfsTRQzmykV13LsKxNXD1oHJ05foR5XavEkqRknZP+b5IMJRsCkVqw7mtsGMsOLgpbaMz0sDJE5zyQEBTqPuWcrRGCKGOhOtK87LUu7B/PlzZBzUHQvAQ5ehqLpAiRAiRdbFRsCoELoW9+L7F6kPZNuDmB7fPKaeFDi2HpNvKz+sMU04lCSHUYzDA359B2DTISId67ygfFJxM+54qRYgQInv+GQ07f4LaQ5RDuEm3lcWxLuyA89uV+zh7Kbc7eipHP4LvD6hdNUBpPQ9KcTLiBNw8BW6+yhEcIYQ6Uu/B7gmw9Rvl++INocWXyukaE5AiRAiRPed3KH0GUuIhoBn0WPnfc8kGA0RHwum/4cBC5QgKKKdlOs8F3wrKGJU752FCNeVnwUOhQkcoXD1XX44Q4hExJ5QjnXumKEcvX98CeYoY/WmkCBFCZF9aMkytC7fOwFuHIG/RZ9839R4cXg5Re8DTX2khffucMr7k3FbQOSmnbi7sVGbs1B4C9d8FV+9cezlCiCfcvQlT64M+BYZHKh8gjCg7798yRVcIobB3go7TYXkfmFAdSjQEvwr3u7pqQGunDHqLjYIz/8DVA/8+VquDPEWVgbP1RihjQ1y8lJk0eyYr56f3TFIKlqBBys+FELnL3hkqdoTQicoHiNIt1U4kR0KEEE9IjocDi2DDR0//uc4Jyryk/AEr2QQ02hfP2ImPhsMrleLl/DblU5hXCdPkF0L8V8J1mN3y3/FbnedChVeM+hRyOkYIYTzpKXB8HRSto0zFfdBrxJCh9BnJjuQ4GFdJGSfSbpwx0wohnuXIKvh9BNjZKyvvlmxiklW75XSMEMJ4dI5Q6VXjbtPJExp9DBs+hMCuUDTYuNsXQiju3lLGaJ3fpiwBUbyhcvTDzGasSREihMhdtQbC0V9heW9oPQbKtVc+oQkhjOPYWvjtDUhNBHtXqNEfWo3J/hFME5LTMUKI3JdwDf2PNUiPv0fybQdcen2GfeNByuBXIUTWJN2BuCuwfx6cXA9xl5TivvHHyoBxB5dciZGd929tVp5gypQpBAYG4uHhgYeHB8HBwaxfv/4/9zMYDLRu3RqNRsNvv/2WlacQQtiApHPXObXUnXN/+hG9Jy9nh43nSocq3PmkC+kr31V6kcRdVnqTCCGezmCArd/Bd8WV6fWRi5RB4z1WQpf54Fsu1wqQ7MrS6ZjChQszZswYSpUqhcFgYN68eXTo0IHIyEgqVKjw8H7jxo1DIwtbCSGeQVegwMPrfiM/Iv3cEe7u2kL8ysNcW3mYQnUW4lEkWVlkr2JHKN0KvANkwTwhHnX9qNINNegNKNcOClY1+6LjSTk+HePl5cUPP/zAgAEDADhw4ABt27Zl3759FChQgNWrV/Pyyy9nentyOkYI25B04ADRH40k9cIF0OlwDAgg5cSJhz8vs+hztCdWKQPr0pPBLT/kKwXeJcGrpHK9eMN//+jq05VGa+lJYOeorCCcEg86Z0hJUNbQSLun9CoJaCaN04TlO/obrOgD755UGgaqLFdnx+j1elasWMHdu3cJDlZGuN+7d4/u3bszadIk8udXf4cIIcyXc5UqlNywnrToaBK2biXl5CmcypXDvkB+PFq3RluqFFTvqnRnPb9dWRX01lm4sl/pOZKaCK6+0OA9uHoIjv2m3PY0dg7K1GIHN0iOVS4bvAd13gJtls5KC2EeruyHXePUTpFjWS5CDh8+THBwMMnJybi5ubF69WrKly8PwDvvvEOdOnXo0KFDpreXkpJCSkrKw+/j4+OzGkkIYcHsCxbEq3v3Z9/BwQXKtFK+HjAYlIJk2xhY/wFo7ZUurAFNlQJDnwqO7pCvjHL9wawAjQYSY2DnOGXRvptnoP14GRArLEtyHMxorFxv9LFZHAXJriwXIWXKlOHAgQPExcWxcuVK+vTpw7Zt2zhz5gybN28mMjIyS9v79ttvGT16dFZjCCFsmUYD+QKg00yo1ge8ioNn4affV+v0+PduvtDqGyhYBX4dCD6llWXOhTB3+nQIn6n0/XD0gJZfQ7XeaqfKkRyPCWnWrBklS5bE2dmZ8ePHo33k0KZer0er1VK/fn22bt361Mc/7UiIv7+/jAkRQpjeurfh4BLov1EpSoQwZ+Ez4Y93lem3Lb+BPP5qJ3qMKh1TMzIySElJYfTo0YSEhDz2s0qVKvHzzz/Trl27Zz7e0dERR0fza6AihLABrcYoC/Gt6AuDd4Kjm9qJhHg2u/vvlXfOm10Bkl1ZKkJGjhxJ69atKVKkCAkJCSxevJitW7eyceNG8ufP/9TBqEWKFKF48eJGCyyEEEZj7wSdZinLm28aDS/9oHYiIZ6tbBtYOxSuHYa0ZOX/r4XLUhESExND7969uXr1Kp6engQGBrJx40aaN29uqnxCCGFa3iWVmTJbvoHgIZC3mNqJhHi6qFDlUucMqXdtrwiZNWtWljZuZh3hhRDi6Wr0V46EnP5bWdtGCHPkdv9sQ+sxVtPnRibICyFsW+pdmNZAue5dUt0sQjyPmy+ggdhLaicxGllFVwhhM2KTYzkdexp7rT3OOmf0Bj0up/6maOxFNA0+gJJNnr+BjAw4txmK1gV759wJLcQDefwhsCtELoCGH4LOQe1EOSZFiBDC6qXoU/gh/AeWnVz21J8XKVyQKifm0vLqHuoXqI2mRj/wKPjvHdKSIXo/7J+vTOl9oGxbqNoLSreUdW1E7igaDIeWKivnuvupnSbHpAgRQli9cRHjWHV6Fe/VeI/6heqjN+hJSk9Cp9VxM+km648s4MT1/axNv0CN06dov38yeAfgkKcIL92+jiY6Uum8+qQbJ2FJVwhoDp1mgHPe3H9xwrZ43G/Kt38eNPxA3SxGIEWIEMLqXUm8Qum8peldvvdTV/huUFgZE7Lryi5+ifiJUU6nMHAb4m/zi70965qOwrF4A/ApqxzxMBhAq1PWnTm5AVYPgumNoc1YZXE8IUwh+gAs7qxc9yqhahRjkYGpQgir16lUJ47dOsYnuz4hTZ/2zPvVLVSX5e1XsbdHOEvbLKVh4YZcNaTR/eYWEr1LKOfg7eyVywfdocu0gte3gnsBWNgJlveBqwdz54UJ2xK5QFmIsWg9qPSq2mmMQooQIYTVa+jfkG/rf8uG8xvou7EvYVfD0Gfon3l/J50TFfJVYGLTicxvPZ9Td07R+tfWzy5gvIpDvz+hw2Rl7Mi0BjD/ZWWBPCGMRXe/L0i1XurmMCIpQoQQNqFtibZMbzGd+JR4Qv4KocqCKnwT9s0LH1fVtyo6jY7YlFh6ru/JpYRnTI/UaKBqDxgWqXRhjb0IE2vA5GBY9xZc3K2cxhEiuxJjlEsrORUDRljAztiyswCOEEJklsFg4PDNwwzYOIB8zvlY32l9ph535OYR3tv2Hm72bixvtxyt5gWf4ZLj4fhauLQXzm1VipL8lZSFx4o3yPkLEbZnVguIj4bhkcppQTOTnfdvORIihLApGo2GQJ9ARtUZxeXEy4RGh2bqcRXzVeTLul9y8s5JIq5HvPgBTh5QtSe0Hw/DD0DvNXDjFMxrp5yqsaKGUyIXnN8Bl8Ig7hLcuah2GqORIkQIYZNaF2tNUIEghm4ayszDM7maePWFS03oDco4khR9StaeTKuFEo3goyh4ZTrcOgNT68LxddlML2yGwQChk2BxF8hXGgbtgHwBaqcyGjkdI4SwWan6VH7c9yMrTq0gLSMNZ50zXk5eONo54uviS1CBICp4V8CAgf3X9zPt0DQAInpG4GCXg26VSXdg7TClCKn/LjT9zEivSFidTV/Ajh+VTqlNPwPPwmoneqbsvH9LESKEsHl3ku9w8MZBLsZfJC4ljqT0JC7GX2Tf9X0kpScB4GrvStsSbRlSZQh5nYzQlCxDD2uHw4GF4OwFPmUgaDCUawdau5xvX1i+k+thyWvQ/Auo+5baaV5IihAhhDCitIw0riZeRaPRUNC1IHbGLg4MBjj5J1zYpfQWubgT8gdCjxXgnt+4zyXMX1oyXNkHZ7coA08Pr1RmXQ3ZaxHLAkgRIoQQliwqDFb0BXsneGUaFK5pEW8+wkgm14GYo+DoqTQly1cK2vwIhaqpnSxTZHaMEEJYsiJB0H+Dcn1Wc5hWH079Jf1FbEWVbsplh4kw8hK8vsViCpDskiJECCHMSd6i8GYY9PwVnPIoa4UseBnuXFA5mDC54KFK+//z22zmCJgUIUIIYW50DhDQFPqsg9eWwO3zSiv489vVTiZMJT0FVoVAwlWbWgRRihAhhDBXGg2UfQkGbYeC1WBRZzjzj9qphCkk3YEjq5TrF3f926LdykkRIoQQ5s45D3RbCsUbwpJucHKD2omEsbnnh56rIKA57J4Af32qdqJcIUWIEEJYAnsn6LoQSrWAZT3gxJ9qJxLGFtBUaV4HkHhd3Sy5RIoQIYSwFDoH6DxXaQG/tBscXKZ2ImFsRYMhbzFlgKoNkCJECCEsiZ09dJwBFV+F1a/Dlm9kCq+1ycgARze1U+QKndoBhBBCZJGLF3SaCX7llbVF3HyhZojaqYQxXNkPcVFQsKraSXKFFCFCCGGJNBpl/ED8VdjwMfjXhvwV1U4lcsJggDVDoEBl5UiXDZDTMUIIYclafq00OPvrf2onETkVdwlijkF6qnLazQZIESKEEJZM5whNR8G5rXB+h9ppRE54FIbSreHGcdj2nbKAXYZe+ZmVjvuRBeyEEMLSGQwwtZ5SkPT/C+zkTLtFW9JNWV0ZIH8lpTg5vw3sHJR+IrVeh5oD1M34FNl5/5b/qUIIYek0Gmg7DmY1g2O/QSXbGE9gtTpMUrqnOrrD8XWQlgR1hitF5tWD8McIKFAFCldXO2mOyZEQIYSwFlPrgU856DRD7STCVDL0MLEG3D4HbX+GGv3VTvRQdt6/ZUyIEEJYi4BmcHaz1Y4fEIDWDpp/qVy3gq65UoQIIYS1KN4A7t2EmONqJxGmlL+Scungom4OI5AiRAghrEWRYLBzVAYxCuuVtyiUfxkuhqqdJMekCBFCCGtxfgfoU5SBjMK6OecFdz+1U+SYFCFCCGEN7t2GxZ1BYwcVO6mdRphKeirsnaH0EPEtr3aaHJMpukIIYQ20OnDyVC4d3dVOI0whLQnmtYPL4VCt978DVC2YHAkRQghr4OQBvdfAvVtwaqPaaYQpHFikFCD9N0L7CeCcR+1EOSZHQoQQwlr43Z81sWcyVOmmbhaRdemp8M/n4JIXvEuBZ2GlEd2V/RA6Ce6cV+7nHaBqTGOSIkQIIayFIUO5rNFP3RwCMjIgNQFSEiA5XrlMSYCUuH+vP3l7zAm4dVoZ12PQP749rU6Zgh3YFVzzqfOaTECKECGEsBZ29uDsBee3K6dl0lOhwivgZ/kDGHNNRgak3X2iSIi///Vk8fCs2xOUAuSZNMq4HUd3cPT493r+ivDS91CiMSTdgbjLkHAV/CqAe0HQWt8ICmnbLoQQ1mTNUIhcoFx39oLkWGj9PdQaqGqsXKNPVwqD5Lisfz0oLHjO26KD23+LByePR27LxO0OblZZUMgCdkIIYevqj1De5OoMA1cf2PgxrP8Q/GtBgcpqp3uxh0VE7HMKhucUGc87AuHgrswgevQrT5H7158sIDyffrRCa5dru8IWyJEQIYSwZvp0+KUyBDRRZlSYmsEAafcgKVY5pZAc+/j1Fx2NSE189rafVkRk9svRA+zkc7cpyZEQIYQQj7PTQR5/2D8/a0VIWvK/BURyrFJEZPZ6RtrTt+ngBk55njgSUVSKCBsm/6JCCGHtyneAqFA48Qek3nv6EYonr6c/o/W7zlnpT+GcVykonPNAvlL/Xne6/7P/XPdUBs4K8QgpQoQQwtoVb6BcLu2uXGrt/1so5PFXVmd9ssB49LpTHrB3yv38wmpJESKEENbOrwK8d0ZZ3M45L9i7KE2whFCZFCFCCGEL3HzUTiDEf1jfRGUhhBBCWAQpQoQQQgihCilChBBCCKEKKUKEEEIIoQopQoQQQgihCilChBBCCKEKKUKEEEIIoQopQoQQQgihCilChBBCCKEKKUKEEEIIoQopQoQQQgihCilChBBCCKEKKUKEEEIIoQqzW0XXYDAAEB8fr3ISIYQQQmTWg/ftB+/jmWF2RUhCQgIA/v7+KicRQgghRFYlJCTg6emZqftqDFkpWXJBRkYG0dHRuLu7o9Fo1I7zVPHx8fj7+3Pp0iU8PDzUjmN1ZP+aluxf05F9a1qyf00rp/vXYDCQkJBAwYIF0WozN9rD7I6EaLVaChcurHaMTPHw8JBfBBOS/Wtasn9NR/atacn+Na2c7N/MHgF5QAamCiGEEEIVUoQIIYQQQhVShGSDo6Mjo0aNwtHRUe0oVkn2r2nJ/jUd2bemJfvXtNTYv2Y3MFUIIYQQtkGOhAghhBBCFVKECCGEEEIVUoQIIYQQQhVShAghhBBCFVKEvMDXX39NnTp1cHFxIU+ePP/5+cGDB+nWrRv+/v44OztTrlw5fvnll2dub9euXeh0OqpUqWK60BbCGPv2119/pXnz5vj4+ODh4UFwcDAbN27MpVdg3oz1f3fr1q1Uq1YNR0dHAgICmDt3runDW4AX7V+A4cOHU716dRwdHZ/5O79x40Zq166Nu7s7Pj4+dOrUiQsXLpgstyUw1r41GAyMHTuW0qVL4+joSKFChfj6669NF9xCGGv/PnDmzBnc3d2fua3nkSLkBVJTU+ncuTNvvPHGU38eERGBr68vCxcu5OjRo/zvf/9j5MiRTJw48T/3jY2NpXfv3jRt2tTUsS2CMfbt9u3bad68OX/++ScRERE0btyYdu3aERkZmVsvw2wZY/+eP3+eNm3a0LhxYw4cOMDbb79NSEiIFHq8eP8+0L9/f7p27frUn50/f54OHTrQpEkTDhw4wMaNG7l58yYdO3Y0RWSLYYx9C/DWW28xc+ZMxo4dy4kTJ1i7di21atUydlyLY6z9C5CWlka3bt2oX79+9sIYRKbMmTPH4Onpman7vvnmm4bGjRv/5/auXbsaPvnkE8OoUaMMlStXNm5AC2aMffuo8uXLG0aPHm2EZNYhJ/v3gw8+MFSoUOGx+3Tt2tXQsmVLY0a0aJnZv8/6nV+xYoVBp9MZ9Hr9w9vWrl1r0Gg0htTUVCMntTw52bfHjh0z6HQ6w4kTJ0wTzgrkZP8+8MEHHxh69uyZpb8zj5IjISYQFxeHl5fXY7fNmTOHc+fOMWrUKJVSWYen7dtHZWRkkJCQ8Nz7iGd7cv+GhobSrFmzx+7TsmVLQkNDczuaVapevTparZY5c+ag1+uJi4tjwYIFNGvWDHt7e7XjWbR169ZRokQJfv/9d4oXL06xYsUICQnh9u3bakezGps3b2bFihVMmjQp29swuwXsLN3u3btZtmwZf/zxx8PbTp8+zUcffcSOHTvQ6WSXZ9fT9u2Txo4dS2JiIl26dMnFZNbhafv32rVr+Pn5PXY/Pz8/4uPjSUpKwtnZObdjWpXixYvz119/0aVLFwYNGoReryc4OJg///xT7WgW79y5c1y8eJEVK1Ywf/589Ho977zzDq+++iqbN29WO57Fu3XrFn379mXhwoU5WkzQJo+EfPTRR2g0mud+nThxIsvbPXLkCB06dGDUqFG0aNECAL1eT/fu3Rk9ejSlS5c29ksxO7m5b5+0ePFiRo8ezfLly/H19c3pSzFLau5fW2Cq/fss165dY+DAgfTp04fw8HC2bduGg4MDr776KgYra2ad2/s2IyODlJQU5s+fT/369WnUqBGzZs1iy5YtnDx50mjPYy5ye/8OHDiQ7t2706BBgxxtxyY/lr/77rv07dv3ufcpUaJElrZ57NgxmjZtyuuvv84nn3zy8PaEhAT27dtHZGQkQ4cOBZRfDoPBgE6n46+//qJJkyZZfg3mKjf37aOWLl1KSEgIK1as+M/pA2uS2/s3f/78XL9+/bHbrl+/joeHh1UeBTHF/n2eSZMm4enpyffff//wtoULF+Lv709YWBi1a9c22nOpLbf3bYECBdDpdI99+CtXrhwAUVFRlClTxmjPZQ5ye/9u3ryZtWvXMnbsWECZiZSRkYFOp2P69On0798/U9uxySLEx8cHHx8fo23v6NGjNGnShD59+vxn+peHhweHDx9+7LbJkyezefNmVq5cSfHixY2Wwxzk5r59YMmSJfTv35+lS5fSpk0boz23Ocrt/fu0UwN///03wcHBRstgToy9f1/k3r17aLWPH5C2s7MDlA8r1iS3923dunVJT0/n7NmzlCxZEoBTp04BULRo0VzLkVtye/+Ghoai1+sffr9mzRq+++47du/eTaFChTK9HZssQrIiKiqK27dvExUVhV6v58CBAwAEBATg5ubGkSNHaNKkCS1btmTEiBFcu3YNUP6Q+Pj4oNVqqVix4mPb9PX1xcnJ6T+325qc7ltQTsH06dOHX375haCgoIf3cXZ2xtPTU5XXZS6MsX8HDx7MxIkT+eCDD+jfvz+bN29m+fLlzx2XYytetH9B6Z+QmJjItWvXSEpKenif8uXL4+DgQJs2bfj555/54osv6NatGwkJCXz88ccULVqUqlWrqvTK1GeMfdusWTOqVatG//79GTduHBkZGQwZMoTmzZvbxKnx5zHG/n1wVOmBffv2PfX97oWyPJ/GxvTp08cA/Odry5YtBoNBmb70tJ8XLVr0mduUKboKY+zbhg0bPvU+ffr0UeU1mRNj/d/dsmWLoUqVKgYHBwdDiRIlDHPmzMn112KOXrR/DYZn//88f/78w/ssWbLEULVqVYOrq6vBx8fH0L59e8Px48dz/wWZEWPt2ytXrhg6duxocHNzM/j5+Rn69u1ruHXrVu6/IDNjrP37qOxO0dUYDFY2+kkIIYQQFsEmZ8cIIYQQQn1ShAghhBBCFVKECCGEEEIVUoQIIYQQQhVShAghhBBCFVKECCGEEEIVUoQIIYQQQhVShAghhBBCFVKECCGEEEIVUoQIIYQQQhVShAghhBBCFVKECCGEEEIV/wfMxG/lqEE4BQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"special for CA, we will cut some of Nevada out of the convex representation of CA\")\n",
    "NVpts = [Point(-120,42), Point(-120, 40), Point(-119,39), Point(-118, 38),Point(-114.6, 35) ]\n",
    "NVtriangle = Polygon(NVpts)\n",
    "plotPoly(NVtriangle)\n",
    "plotPoly(convexMAP)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "48b0b450-0079-4f28-affc-16681300e8d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is CA map after the convex hull modification\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#special for CA\n",
    "stateHull = convexMAP.convex_hull.difference(NVtriangle)\n",
    "MAPhull = stateHull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(stateHull)\n",
    "plotPoly(MAPhull)\n",
    "print(\"Here is\",STATE,\"map after the convex hull modification\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8393e96a-75c6-48ec-94e8-1f6fe58d31df",
   "metadata": {},
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()  #nomenclature equivalency\n",
    "bannedCountyList = [list() for c in range(nCounties)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5ae791cd-d399-456f-a0cf-d3f215589177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "see the CA-bg code for a further narrowing of neighbor county lists.  Not done here.\n"
     ]
    }
   ],
   "source": [
    "print(\"see the CA-bg code for a further narrowing (banning) of neighbor county lists.  Not done here.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "34da8f82-5333-4e6a-8a4a-65078e588f0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now figure out which counties are close enough to each other for unit-unit distance calcn\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT, despite the \"unit\" nomenclature\n",
    "print(\"Now figure out which counties are close enough to each other for unit-unit distance calcn\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if STATE == \"IL\":\n",
    "    print(\"copy IL code here\")\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "    \n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist and cc not in bannedCountyList[c] and c not in bannedCountyList[cc]:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bcf72585-4b62-4edb-be72-41d3a9360d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ve0555p+d9tNsh1JvIdJjNhJNTU2qr69XV9enl9tdLpdqamo0btzesMfsYAY93w1ioPNdcW6xFs9anBJ1PsKNXUVFxaA/n8xzXrL3u0SjzscZ6ncc0M1/aAw5r/v/u/SQvrD+NvX/jo3eFmuqF6tu95Mh572v+PKKiALIUKrWxVproaPjJW3fUatQ/22fm/aQCgvnUeE0jSW60mQmVjiNtEZKoipNNjU1adWqVQOuy8/fo4rzGgZYE3zMhjLY+e6R6yojCiCpWuE03NhJ0sKFCyMKIFQ4TQzCxyl+w9TFP3816C+AMzlkaN2I21Sojwa8D+iXTfMmnaV2x8D/422yqSi3SPUL6oMOzHgwDVNtP98Udsqjw+1U8eKZA34YmKZfb637YtBfT8FscjqLddGFDbLZhscHNWD6/frgsurQ0x5tNmUVFemza9ck5MPUMAzdf//9QX+1n7FWs2Y/p5ycYyEeDQh/zA52vrNJKnaP0N8Wzw18a2s6CT92vVwulxYtWiS7fdg8UZBSKDJ2yqaWj0MeiJI00/6eikIED0lqHJETMnhIvYV32o61qbGjMcae9hdrrQWvd3OY4CFJpny+A/J6N8fQSyC9JLpGymBaW1tDfni63R1yOkMFD2mwY3aw850p6UDncW1q+Ti6TqeIcGN3WldXl1pbWy3qEWKR0eGj40joA1GSCuUNu/5ghH/5DDY/fihirbXg83VE9PORtgMyQaJrpAymuzt0sYycnE8i2kaoY3aw81207VJNuLEbSjskV0aHj8LRI8Ku75An7PqCCMsEDzY/fihirbXgdEb2cFKk7YBMkOgaKYPJyws9o6GnZ2RE2wh1zA52vou2XaoJN3ZDaYfkyujwMatsrErcI0LeVtlsTFG78mWGaFF5vEdFfjPkvHebbCrOLVZlYWWcevypWGsteDwz5XQWq/+kstNscjpL5PHMjK2jQBpJdI2UwZSWloa8F97ZWSifL3fAr/841bmwx+xg5zubeme9zCobG223U0K4sTvN5XKptLTUoh4hFhkdPhx2m+6Z3/vk80Dzug3Ztb/qnlPr+rdwSFpyzrWn3vWf9y5Ji2ctjvvDplLstRZsNofOKb/79Lu+ayVJ55TfxcOmCTaUegvpYii1FqKRiLFLdI2UwdjtdtXU1IRaq90fzDzVjeiP2cHOd5J0z/yKtHzYVBps7HrV1NQM+4dNY60LZZWMnu1y2tDqfEyQapaHrPNx5rz3RIq11sLAdT5KdE75XRHVDMDQxVpvIZXFWmthMIkeu1SuVTFwnY/Ij9lM3u+kxO976SzWulCxYqrtAPyGqU0tH6vjyHEVju699Bj0F4Dhl1rXSd3tUl6RVHqhdMYVjcHmvSdSrLUWTNN/avZLh5zOQnk8M7nikWDxqreQiuJVayEUq8YuWbUqTjMMQ62treru7lZeXp5KS0sDf7XHeswOer5Lc+HGbriKtS5UPBA+gCTK5HoLia61kMljByRKrHWh4oU6H0ASZXK9hUTXWsjksQMSJda6UMlA+ADiLJPrLSS61kImjx2QKLHWhUoGwgcQZ5lcbyHRtRYyeeyARIm1LlQyED6AOMvkeguJrrWQyWMHJEqsdaGSgfABxFkm11uIptaCafp1+PAGtbU9r8OHN8g0B68YnMljF09+09Rbh4/oufbDeuvwEflTa97AoAzDr73vvqOdbzVo77vvyDAiqyaNgUVTFyrR9XkixWwXIEEyud7CYLUWBq4vU6xzyu+mVkWMXjjo1Y+a9+mA70RgWYkzW/eWT9AVBZ7kdSxCzRvX6dWVj6n740OBZXljx2nuDTepfPaFSexZ+husLlSia6Qw1RZIEZlcbyFUrYWOjpe0fUetFKJSx+emPRRRAMnksRuqFw569Z0dfw9ZA+W30z6T0gGkeeM6Pb/ivpDrr6y7kwASo1B1oRJdn0eK7vM7K6bfBCAsh92mqsn5ye5GQtjtdpWVlQUtM02/3m/+qfoHD51aZtP7zT9TQUH1oEWzMnnshsJvmvpR874wIyvd1bxPNePccoT67pokMgy/Xl35WNg2r/3XY5o8c7bsFhVwzEQ2u00jJnuClhmGofr6+rA/V19frylTplhWrI1nPgDETW9VzrYwLUz5fAfk9W62rE+ZYoO3O+hWS1+mpP2+E9rgTc2vlN+3892gWy0DOfLRIe3b+a5FPRo+El2fZygIHwDixufriGs7fKqj52Rc21mt23s4ru0QuUTX5xkKwgeAuHE6C+PaDp8qzInsLnmk7ayW5xkT13aIXKLr8wwF4QNA3Hg8M+V0Fqv/RNnTbHI6S+TxzLSyWxlhjidPJc7ssDVQxjuzNcdj3QdINCZMPU95Y8N/sdno/HGaMPU8i3o0fCS6Ps9QED5SAHPewzMMU/t2Hdb7m9u0b9dhGUZKTdDCGWw2h84pv/v0u75rJUnnlN/FtyoPgcNm073lEySFroHys/IJKfmwqSTZ7Q7NveGmsG0uvf6muDxsOpQaM6f5DVPrd3+kP7+9T+t3fyR/BpxvoqnPYxWm2iYZc97D272tQ28+3ayjXl9g2SiPU5dcU67JX+DSfaoauM5Hic4pvyuiabYIbaA6H+Od2fpZGtf5GJ0/TpdeH59zXiw1ZjK9vgx1PsIYTuGDOe/h7d7WofpHd4RcX/PdaQSQFGaa/lOzXzrkdBbK45nJFY848ZumNni71dFzUoU5WZrjyUvZKx4DMQx/7+wX72HlecZowtTz4nLFI5YaM/U7DujmPzSGrKHyyHWVGRFAQtXniQfqfKQB5ryHZxim3ny6OWybv61qVtn5BbIP88JTqcpmc2jMmDnJ7kZGcthsumjM6GR3Y8jsdocmnjc9rtuMpcaM3zD1k9VNYWuo/GR1k75SUZz2he4Gqs+TlH4kuwPDFXPewzvQ7A261TKQ7sM+HWj2WtMhACktlhozm1o+DrrV0v8npQOdx7Wp5ePYOwpJhI+kYc57eEe7wgePaNsByGyx1JjpOBI6eAylHQZH+EgS5ryHN8rljGs7AJktlhozhaNHRPSzkbbD4AgfScKc9/BKyj0a5QkfLPLGOFVS7rGmQwBSWiw1ZmaVjVWJe0TYGiol7t4vN0R8ED6SxMo57+nIbrfpkmvKw7a5eGE5D5sCkBRbjRmH3aZ75leE+UnpnvkVaf+waSphqm2cmH6/jm3ZqpMHDyqroEC5F8yQzTF4cEj0nPd4SOZXmw9U5yNvjFMXL4yszkeor5dGBjD8Uus6qbtdyiuSSi+UhmlYj1oGj10sNWYyvc5HolHnw2JdL7+s9vuW6WTbpzt7VnGxiu5cKtfllw/684ma8x4PqXAwGobZO/uly6dRrt5bLZFc8fhkxyF5V++Wv7MnsMzhzpFn/mSNnBb+lhdSXNPzUv1iqWv/p8tc46Wan0sVVyavX+lgGIxdLDVmkvnHVrojfFio6+WXte+2RVLfYTxV8GfCA/dHFEBSUToX3flkxyF99IedIdfnXzeVAJKump6XVn1ToQpJaeHvM+ZDNO4YOyRQNJ/fPPMRA9PvV/t9y/oHDymwrP2+ZTL96fddLYMV3ZF6i+6k4vcemIYp7+rdYdt4V38oMwX7jkEY/t6/2sPtmfVLetshGGOHFEL4iMGxLVuDbrX0Y5o62damY1u2WtepOEnnoju+ls6gWy0D8Xf65GvptKhHiJvWdcG3C/oxpa59ve0QjLFDCiF8xODkwYNxbZdK0rnojnEkfPCIth1SSHd7fNsNJ4wdUgjhIwZZBQVxbZdK0rnojn10TlzbIYXkFcW33XDC2CGFED5ikHvBDGUVFwceLu3HZlNWcbFyL5hhbcfiIJ2L7jjL3HK4wwcLh9spZ5nboh4hbkov7J2ZEW7PdE3obYdgjB1SCOEjBjaHQ0V3Lj31ps8Bfep90Z1LI6r3kWrSueiOzW6TZ/7ksG0888+WzW6TYRhqaWnR9u3b1dLSIsMwLOplZPyGX5vbNuuvH/5Vm9s2yz/cHwa0O3qnhEoKuWfWLM+YmhVxxdjFh+GXWt6Utj/b+8/hfkwOEVNt4yDWOh+pLBXqfAzVwHU+nPLMP1sjp41TU1OT6uvr1dXVFVjvcrlUU1OjioqKZHQ5yJrWNVq+abnaj316D74ot0hLZi1RdWl1EnuWAgasVTGh98OTqaLhMXZDNwxqpMSCOh9JMNQKp+kgnYvuhKpw2tTUpFWrVoX8uYULFyY1gKxpXaO61+tk9pkWaTv1F+qKL68ggGRwlc6EY+yiR42UQRE+gDAMw9D9998fdMWjL5fLpUWLFslut/7OpN/wa94f5wVd8TiTTTYV5RapfkG9HHxgAIln+KX7p4WZqmzrvQKyaPuwDnEUGQPCaG1tDRs8pN6DqLW11aIeBWvsaAwZPCTJlKm2Y21q7Gi0sFfAMEaNlLgjfGDY6e7ujmu7eDt4LLK6MJG2AxAjaqTEHeEDw05eXl5c28VbQW5kdWEibQcgRtRIiTvCB4ad0tLSQe9HulwulZaWWtSjYJWFlSrKLQo8XNqXTTYV5xarsrDS4p4BwxQ1UuKO8IFhx263q6amJmybmpqapDxsKkkOu0NLZi2RpH4B5PT7xbMW87ApUpZhmNq367De39ymfbsOy0j3L3GkRkrcRXV2/fGPfyybzRb0mjJlSmD98ePHVVtbq/z8fOXl5WnBggVqb+ceGFJPRUWFFi5c2O8KiMvlSvo0W0mqLq3Wii+vUGFuYdDyotwiptkipe3e1qHf37lOf/rVNr3yuyb96Vfb9Ps712n3to5kdy02FVf2Tqd19alv5BrPNNshiGqq7Y9//GM9++yzWrNmTWBZVlaWxo0bJ0m6+eab9cILL2jlypVyu9265ZZbZLfb9dZbb0XcIabawkqGYai1tVXd3d3Ky8tTaWlp0q54DMRv+NXY0aiDxw6qILdAlYWVXPFAytq9rUP1j+4Iub7mu9M0+QuFIdenBWqkhBTN53dWtBvPyspScXFxv+WdnZ363e9+pyeeeEJz586VJD3++OOaOnWqNmzYoDlz5kT7q4CEs9vtKisrS3Y3QnLYHZpZPDPZ3QAGZRim3ny6OWybv61qVtn5BbKnSZHCAdkdUtklye5F2ov6T7zm5maNHz9eZ599tq699lrt2bNHkrR161adOHFC1dWfXg6eMmWKJk2apPXr14fcns/nU1dXV9ALAJBeDjR7ddTrC9um+7BPB5q91nQIKS2q8DF79mytXLlS9fX1euSRR9TS0qJLLrlER44cUVtbm3JycuTxeIJ+pqioSG1nfOdJX8uWLZPb7Q68Jk6cOKT/EABA8hztCh88om2HzBbVbZevfvWrgX+fPn26Zs+erdLSUq1atUojR44cUgeWLl2qurq6wPuuri4CCACkmVEuZ1zbIbPF9GSdx+PROeecow8++EDFxcXq6emR1+sNatPe3j7gMyKnOZ1OuVyuoBcAIL2UlHs0yhM+WOSNcaqk3GNNh5DSYgof3d3d2r17t0pKSjRjxgxlZ2dr7dq1gfW7du3Snj17VFVVFXNHAQCpy2636ZJrysO2uXhheXo/bBoBv+HX5rbN+uuHf9Xmts3yG/64bt8w/Nr77jva+VaD9r77jow4b98qUd12+cEPfqD58+ertLRU+/fv1z333COHw6F/+Zd/kdvt1o033qi6ujqNHTtWLpdLt956q6qqqpjpAgDDwOQvFKrmu9P05tPNQQ+f5o1x6uKF5ek/zXYQa1rXaPmm5UFfDFmUW6Qls5bEpTZP88Z1enXlY+r++FBgWd7YcZp7w00qn51e1VWjqvPxjW98Q2+88YY++ugjFRQU6OKLL9a//du/afLkyZJ6i4zdcccdevLJJ+Xz+TRv3jw9/PDDYW+79EWdDwBIb4Zh9s5+6fJplKv3VkumX/FY07pGda/XyVTwR+rpqsSxFgds3rhOz6+4L+T6K+vuTHoAiebzO6rwYQXCBwAgnfgNv+b9cV7QFY8z2WRTUW6R6hfUD6lIoGH49ZvaG4OuePQ1On+cvvOfv5M9iQXPovn8Tp1SjgAApKHGjsaQwUOSTJlqO9amxo7GIW1/3853wwYPSTry0SHt2/nukLafDIQPAABicPDYwbi266vbeziu7VIB4QMAgBgU5BbEtV1feZ4xcW2XCggfAADEoLKwUkW5RYGHS/uyyabi3GJVFlYOafsTpp6nvLHjwrYZnT9OE6aeN6TtJwPhA7Ez/FLLm9L2Z3v/mabzzjORYZjat+uw3t/cpn27DsswUur5cmBApmHq+G6vjr3doeO7vTJTfL912B1aMmuJJPULIKffL561eMjfSG23OzT3hpvCtrn0+puS+rBptJjtgtg0PS/VL5a69n+6zDVeqvm5VHFl8voF7d7W0a/ewiiPU5dck/n1FpC+PtlxSN7Vu+Xv7Aksc7hz5Jk/WSOnhf/rP9kGqvNRnFusxbMWJ6zOx+j8cbr0+tSo88FUW1ij6Xlp1Tcl9d2FTiX/hb8ngCTJ7m0dqn90R8j1Nd+dRgBByvlkxyF99IedIdfnXzc15QOI3/CrsaNRB48dVEFugSoLK4d8xWMghuHvnf3iPaw8zxhNmHpeylzxiObzO6oKp0CA4e+94tEveOjUMptUv0SacoWUIgfGcGEYpt58ujlsm7+talbZ+QUZX/gJ6cM0THlX7w7bxrv6Q42oyJcthfdbh92hmcUzE7Z9u92hiedNT9j2rcIzHxia1nXBt1r6MaWufb3tYKkDzd6gWy0D6T7s04FmrzUdAiLga+kMutUyEH+nT76WTot6hEQifGBoukMX1BlSO8TN0a7wwSPadoAVjCPhg0e07ZDaCB8Ymryi+LZD3Ixyhf9a82jbAVawj86JazukNsIHhqb0wt5ZLSHmtUs2yTWhtx0sVVLu0ShP+GCRN6b3y76AVOEsc8vhDh8sHG6nnGVui3qERCJ8YGjsjt7ptJL6B5BT72uW87BpEtjtNl1yTXnYNhcvLOdhU6QUm90mz/zJYdt45p+d0g+bInKEDwxdxZW902ldJcHLXeOZZptkk79QqJrvTut3BSRvjJNptkhZI6eNU/51U/tdAXG4nWkxzRaRo84HYmf4e2e1dLf3PuNReiFXPFKEYZi9s1+6fBrl6r3VwhUPpDrTMOVr6ZRxpEf20Tlylrm54pEGqPMBa9kdUtklye4FBmC32zTh3PT5silA6r0FM2KyJ9ndQAJx2wUAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApfhWWyAM0/TL690sn69DTmehPJ6Zstkcye4WAKQ1wgcQQkfHS3q/+afy+doCy5zOYp1TfrcKC+clsWcAkN647QIMoKPjJW3fURsUPCTJ52vX9h216uh4KUk9A4D0R/gA+jBNv95v/qkkc6C1kqT3m38m0/Rb2i8AyBSED6CP3mc82sK0MOXzHZDXu9myPgFAJiF8AH34fB1xbQcACEb4APpwOgvj2g4AEIzwAfTh8cyU01ksyRaihU1OZ4k8nplWdgsAMgbhIwMYhqGWlhZt375dLS0tMgwj2V1KazabQ+eU3336Xd+1kqRzyu+i3gcADBF1PtJcU1OT6uvr1dXVFVjmcrlUU1OjioqKJPYsvRUWztPnpj0Uos7HXdT5AIAY2EzTHGg+YdJ0dXXJ7Xars7NTLpcr2d1JaU1NTVq1alXI9QsXLiSAxIgKpwAQmWg+v7nykaYMw1B9fX3YNvX19ZoyZYrsdu6uDZXN5tCYMXOS3Q0AyCh8KqWp1tbWoFstA+nq6lJra6tFPQIAIDKEjzTV3d0d13YAAFiF8JGm8vLy4toOAACrED7SVGlp6aAP9LhcLpWWllrUIwAAIkP4SFN2u101NTVh29TU1PCwKQAg5fDJlMYqKiq0cOHCfldAXC4X02wBACmLqbZprqKiQlOmTFFra6u6u7uVl5en0tJSrngAAFIW4SMD2O12lZWVJbsbAABEhD+PAQCApQgfAADAUoQPAABgKcIHAACwFOEDAABYitkuADAEhuHXvp3vqtt7WHmeMZow9TzZ7Y5kdwtIC4QPAIhS88Z1enXlY+r++FBgWd7YcZp7w00qn31hEnsGpAduuwBAFJo3rtPzK+4LCh6S1P3xIT2/4j41b1yXpJ4B6YPwAQARMgy/Xl35WNg2r/3XYzIMv0U9AtIT4QMAIrRv57v9rnj0deSjQ9q3812LegSkJ8IHAESo23s4ru2A4YrwAQARyvOMiWs7YLgifABAhCZMPU95Y8eFbTM6f5wmTD3Poh4B6YnwAQARstsdmnvDTWHbXHr9TdT7AAZB+ACAKJTPvlBX1t3Z7wrI6PxxurLuTup8ABGgyBgARKl89oWaPHM2FU6BISJ8AMAQ2O0OTTxverK7AaQlbrsAAABLET4AAIClCB8AAMBShA8AAGApwgcAALBUTOFj+fLlstlsWrRoUWDZ8ePHVVtbq/z8fOXl5WnBggVqb2+PtZ8AACBDDDl8bN68WY8++qimTw+eanb77bdr9erVeuaZZ9TQ0KD9+/fr6quvjrmjAAAgMwwpfHR3d+vaa6/Vb37zG40Z8+kXKHV2dup3v/udVqxYoblz52rGjBl6/PHHtW7dOm3YsCFunQYAAOlrSOGjtrZWV1xxhaqrq4OWb926VSdOnAhaPmXKFE2aNEnr168fcFs+n09dXV1BLwAAkLmirnD61FNPqbGxUZs3b+63rq2tTTk5OfJ4PEHLi4qK1NbWNuD2li1bpp/85CfRdgMAAKSpqK587N27V7fddpv++7//WyNGjIhLB5YuXarOzs7Aa+/evXHZLgAASE1RhY+tW7eqo6NDlZWVysrKUlZWlhoaGvTggw8qKytLRUVF6unpkdfrDfq59vZ2FRcXD7hNp9Mpl8sV9AIAAJkrqtsul112mbZv3x607Fvf+pamTJmixYsXa+LEicrOztbatWu1YMECSdKuXbu0Z88eVVVVxa/XANKe3zS1wdutjp6TKszJ0hxPnhw2W7K7BcACUYWP0aNHa9q0aUHLRo0apfz8/MDyG2+8UXV1dRo7dqxcLpduvfVWVVVVac6cOfHrNYC09sJBr37UvE8HfCcCy0qc2bq3fIKuKPAkr2MALBH1A6eD+dWvfiW73a4FCxbI5/Np3rx5evjhh+P9awCkqRcOevWdHX+X2Wd5m++EvrPj7/rttM8QQIAMZzNNs+85IKm6urrkdrvV2dnJ8x9AhvGbpi5Y3xR0xeNMNvVeAdlcVcEtGCDNRPP5zXe7ALDMBm93yOAhSaak/b4T2uDttq5TACxH+ABgmY6ek3FtByA9ET4AWKYwJ7LHzCJtByA9ET4AWGaOJ08lzmyFeprDJmm8M1tzPHlWdguAxQgfACzjsNl0b/kESeoXQE6//1n5BB42BTIc4QOApa4o8Oi30z6jYmd20PISZzbTbIFhghurACx3RYFHNePcVDgFhinCB4CkcNhsumjM6GR3A0AScNsFAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApbKS3QEASAS/4VdjR6MOHjuogtwCVRZWymF3JLtbAET4AJCB1rSu0fJNy9V+rD2wrCi3SEtmLVF1aXUSewZA4rYLgAyzpnWN6l6vCwoektRxrEN1r9dpTeuaJPUMwGmEDwAZw2/4tXzTcpky+607veznm34uv+G3umsAzkD4AJAxGjsa+13xOJMpU23H2tTY0WhhrwD0RfgAkDEOHjsY13YAEoPwASBjFOQWxLUdgMQgfADIGJWFlSrKLZJNtgHX22RTcW6xKgsrLe4ZgDMRPgBkDIfdoSWzlkhSvwBy+v3iWYup9wEkGeEDQEapLq3Wii+vUGFuYdDyotwirfjyCup8ACmAImMAMk51abUunXgpFU6BFEX4AJCRHHaHZhbPTHY3AAyA2y4AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJaKKnw88sgjmj59ulwul1wul6qqqvTiiy8G1h8/fly1tbXKz89XXl6eFixYoPb29rh3GgAApK+owsdZZ52l5cuXa+vWrdqyZYvmzp2rq666Su+++64k6fbbb9fq1av1zDPPqKGhQfv379fVV1+dkI4DAID0ZDNN04xlA2PHjtUvf/lLff3rX1dBQYGeeOIJff3rX5ckvffee5o6darWr1+vOXPmRLS9rq4uud1udXZ2yuVyxdI1AABgkWg+v4f8zIff79dTTz2lo0ePqqqqSlu3btWJEydUXV0daDNlyhRNmjRJ69evD7kdn8+nrq6uoBcAAMhcUYeP7du3Ky8vT06nU9/73vf03HPPqaKiQm1tbcrJyZHH4wlqX1RUpLa2tpDbW7Zsmdxud+A1ceLEqP8jAABA+og6fJx77rl6++23tXHjRt188826/vrr1dTUNOQOLF26VJ2dnYHX3r17h7wtAACQ+rKi/YGcnBx99rOflSTNmDFDmzdv1gMPPKBrrrlGPT098nq9QVc/2tvbVVxcHHJ7TqdTTqcz+p4DAIC0FHOdD8Mw5PP5NGPGDGVnZ2vt2rWBdbt27dKePXtUVVUV668BAAAZIqorH0uXLtVXv/pVTZo0SUeOHNETTzyh119/XS+99JLcbrduvPFG1dXVaezYsXK5XLr11ltVVVUV8UwXAACQ+aIKHx0dHfrmN7+pAwcOyO12a/r06XrppZf0la98RZL0q1/9Sna7XQsWLJDP59O8efP08MMPJ6TjAAAgPcVc5yPeqPMBAED6saTOBwAAwFAQPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsFRWsjsAAJnINEz5WjplHOmRfXSOnGVu2ey2ZHcLSAmEDwCIs092HJJ39W75O3sCyxzuHHnmT9bIaeOS2DMgNXDbBQDi6JMdh/TRH3YGBQ9J8nf26KM/7NQnOw4lqWdA6iB8AECcmIYp7+rdYdt4V38o0zAt6hGQmggfABAnvpbOflc8+vJ3+uRr6bSoR0BqInwAQJwYR8IHj2jbAZmK8AEAcWIfnRPXdkCmInwAQJw4y9xyuMMHC4fbKWeZ26IeAamJ8AEAcWKz2+SZPzlsG8/8s6n3gWGP8AEAcTRy2jjlXze13xUQh9up/OumUucDEEXGACDuRk4bpxEV+VQ4BUIgfABAAtjsNo2Y7El2N4CUxG0XAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLRRU+li1bppkzZ2r06NEqLCzU1772Ne3atSuozfHjx1VbW6v8/Hzl5eVpwYIFam9vj2unAQBA+ooqfDQ0NKi2tlYbNmzQK6+8ohMnTujyyy/X0aNHA21uv/12rV69Ws8884waGhq0f/9+XX311XHvOAAASE820zTNof7wwYMHVVhYqIaGBn3xi19UZ2enCgoK9MQTT+jrX/+6JOm9997T1KlTtX79es2ZM2fQbXZ1dcntdquzs1Mul2uoXQMAABaK5vM7pmc+Ojs7JUljx46VJG3dulUnTpxQdXV1oM2UKVM0adIkrV+/fsBt+Hw+dXV1Bb0AAEDmGnL4MAxDixYt0kUXXaRp06ZJktra2pSTkyOPxxPUtqioSG1tbQNuZ9myZXK73YHXxIkTh9olAACQBoYcPmpra7Vjxw499dRTMXVg6dKl6uzsDLz27t0b0/YAAEBqyxrKD91yyy36y1/+ojfeeENnnXVWYHlxcbF6enrk9XqDrn60t7eruLh4wG05nU45nc6hdAMAAKShqK58mKapW265Rc8995xeffVVlZWVBa2fMWOGsrOztXbt2sCyXbt2ac+ePaqqqopPjwEAQFqL6spHbW2tnnjiCf35z3/W6NGjA89xuN1ujRw5Um63WzfeeKPq6uo0duxYuVwu3XrrraqqqopopgsAAMh8UU21tdlsAy5//PHHdcMNN0jqLTJ2xx136Mknn5TP59O8efP08MMPh7zt0hdTbQEASD/RfH7HVOcjEQgfAACkH8vqfAAAAESL8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAAS2UluwPIfH7Dr8aORh08dlAFuQWqLKyUw+5IdrcAAElC+EBCrWldo+Wblqv9WHtgWVFukZbMWqLq0uok9gwAkCzcdkHCrGldo7rX64KChyR1HOtQ3et1WtO6Jkk9AwAkE+EDCeE3/Fq+ablMmf3WnV72800/l9/wW901AECSET6QEI0djf2ueJzJlKm2Y21q7Gi0sFcAgFRA+EBCHDx2MK7tAACZg/CBhCjILYhrOwBA5iB8ICEqCytVlFskm2wDrrfJpuLcYlUWVlrcMwBAshE+kBAOu0NLZi2RpH4B5PT7xbMWU+8DAIYhwgcSprq0Wiu+vEKFuYVBy4tyi7Tiyyuo8wEAwxRFxpBQ1aXVunTipVQ4BQAEED6QcA67QzOLZya7GwCAFMFtFwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgqZSrcGqapiSpq6sryT0BAACROv25ffpzPJyUCx9HjhyRJE2cODHJPQEAANE6cuSI3G532DY2M5KIYiHDMLR//36NHj1aNptt8B+IQldXlyZOnKi9e/fK5XLFdduZjrEbOsZu6Bi7oWPsho6xGxrTNHXkyBGNHz9ednv4pzpS7sqH3W7XWWedldDf4XK52KGGiLEbOsZu6Bi7oWPsho6xi95gVzxO44FTAABgKcIHAACw1LAKH06nU/fcc4+cTmeyu5J2GLuhY+yGjrEbOsZu6Bi7xEu5B04BAEBmG1ZXPgAAQPIRPgAAgKUIHwAAwFKEDwAAYKlhEz4eeughfeYzn9GIESM0e/Zsbdq0KdldSklvvPGG5s+fr/Hjx8tms+lPf/pT0HrTNHX33XerpKREI0eOVHV1tZqbm5PT2RSybNkyzZw5U6NHj1ZhYaG+9rWvadeuXUFtjh8/rtraWuXn5ysvL08LFixQe3t7knqcWh555BFNnz49UNSpqqpKL774YmA9YxeZ5cuXy2azadGiRYFljF1oP/7xj2Wz2YJeU6ZMCaxn7BJnWISPp59+WnV1dbrnnnvU2Nio888/X/PmzVNHR0eyu5Zyjh49qvPPP18PPfTQgOt/8Ytf6MEHH9Svf/1rbdy4UaNGjdK8efN0/Phxi3uaWhoaGlRbW6sNGzbolVde0YkTJ3T55Zfr6NGjgTa33367Vq9erWeeeUYNDQ3av3+/rr766iT2OnWcddZZWr58ubZu3aotW7Zo7ty5uuqqq/Tuu+9KYuwisXnzZj366KOaPn160HLGLrzzzjtPBw4cCLz+9re/BdYxdglkDgOzZs0ya2trA+/9fr85fvx4c9myZUnsVeqTZD733HOB94ZhmMXFxeYvf/nLwDKv12s6nU7zySefTEIPU1dHR4cpyWxoaDBNs3ecsrOzzWeeeSbQZufOnaYkc/369cnqZkobM2aM+dvf/paxi8CRI0fM8vJy85VXXjG/9KUvmbfddptpmux3g7nnnnvM888/f8B1jF1iZfyVj56eHm3dulXV1dWBZXa7XdXV1Vq/fn0Se5Z+Wlpa1NbWFjSWbrdbs2fPZiz76OzslCSNHTtWkrR161adOHEiaOymTJmiSZMmMXZ9+P1+PfXUUzp69KiqqqoYuwjU1tbqiiuuCBojif0uEs3NzRo/frzOPvtsXXvttdqzZ48kxi7RUu6L5eLt0KFD8vv9KioqClpeVFSk9957L0m9Sk9tbW2SNOBYnl6H3m9mXrRokS666CJNmzZNUu/Y5eTkyOPxBLVl7D61fft2VVVV6fjx48rLy9Nzzz2niooKvf3224xdGE899ZQaGxu1efPmfuvY78KbPXu2Vq5cqXPPPVcHDhzQT37yE11yySXasWMHY5dgGR8+AKvV1tZqx44dQfeOMbhzzz1Xb7/9tjo7O/Xss8/q+uuvV0NDQ7K7ldL27t2r2267Ta+88opGjBiR7O6kna9+9auBf58+fbpmz56t0tJSrVq1SiNHjkxizzJfxt92GTdunBwOR78nlNvb21VcXJykXqWn0+PFWIZ2yy236C9/+Ytee+01nXXWWYHlxcXF6unpkdfrDWrP2H0qJydHn/3sZzVjxgwtW7ZM559/vh544AHGLoytW7eqo6NDlZWVysrKUlZWlhoaGvTggw8qKytLRUVFjF0UPB6PzjnnHH3wwQfsdwmW8eEjJydHM2bM0Nq1awPLDMPQ2rVrVVVVlcSepZ+ysjIVFxcHjWVXV5c2btw47MfSNE3dcssteu655/Tqq6+qrKwsaP2MGTOUnZ0dNHa7du3Snj17hv3YhWIYhnw+H2MXxmWXXabt27fr7bffDrwuuOACXXvttYF/Z+wi193drd27d6ukpIT9LtGS/cSrFZ566inT6XSaK1euNJuamsybbrrJ9Hg8ZltbW7K7lnKOHDlibtu2zdy2bZspyVyxYoW5bds2s7W11TRN01y+fLnp8XjMP//5z+Y777xjXnXVVWZZWZn5ySefJLnnyXXzzTebbrfbfP31180DBw4EXseOHQu0+d73vmdOmjTJfPXVV80tW7aYVVVVZlVVVRJ7nTqWLFliNjQ0mC0tLeY777xjLlmyxLTZbObLL79smiZjF40zZ7uYJmMXzh133GG+/vrrZktLi/nWW2+Z1dXV5rhx48yOjg7TNBm7RBoW4cM0TfM//uM/zEmTJpk5OTnmrFmzzA0bNiS7SynptddeMyX1e11//fWmafZOt73rrrvMoqIi0+l0mpdddpm5a9eu5HY6BQw0ZpLMxx9/PNDmk08+Mb///e+bY8aMMXNzc81/+qd/Mg8cOJC8TqeQb3/722ZpaamZk5NjFhQUmJdddlkgeJgmYxeNvuGDsQvtmmuuMUtKSsycnBxzwoQJ5jXXXGN+8MEHgfWMXeLYTNM0k3PNBQAADEcZ/8wHAABILYQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFjq/we8ne5xbY1vzgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of neighbor counties by county\n"
     ]
    }
   ],
   "source": [
    "for c in range(nCounties):\n",
    "    plt.scatter(c,len(closeCountyList[c]))\n",
    "plt.axhline(nCounties,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"number of neighbor counties by county\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "17eebb13-8ece-4e1b-b869-b98137926250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the hundreds of close tracts by county out of 58\n",
      "there are 91 hundreds of tracts in the whole state\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the hundreds of close tracts by county out of\",nCounties)\n",
    "print(\"there are\",int(nTracts/100),\"hundreds of tracts in the whole state\")\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    nClose = 0\n",
    "    for cc in range(nCounties):\n",
    "        if cc in closeCountyList[c]:\n",
    "            nClose += len(countyTractList[cc])\n",
    "    plotCenter(int(nClose/100),countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bcaf6ea2-1c3b-48c1-9ad6-937d7b7e4385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\n",
      "working on tract-tract distances for unit 0 out of 9129 . Time = 0\n",
      "working on tract-tract distances for unit 1001 out of 9129 . Time = 665\n",
      "working on tract-tract distances for unit 2003 out of 9129 . Time = 1297\n",
      "working on tract-tract distances for unit 3008 out of 9129 . Time = 1860\n",
      "working on tract-tract distances for unit 4008 out of 9129 . Time = 2358\n",
      "working on tract-tract distances for unit 5008 out of 9129 . Time = 2760\n",
      "working on tract-tract distances for unit 6009 out of 9129 . Time = 3102\n",
      "working on tract-tract distances for unit 7011 out of 9129 . Time = 3355\n",
      "working on tract-tract distances for unit 8016 out of 9129 . Time = 3521\n",
      "working on tract-tract distances for unit 9018 out of 9129 . Time = 3583\n",
      "All done; total elapsed  3583 seconds for CA with 52 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our maxD was 6.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\")\n",
    "defaultD = int(maxD+1)\n",
    "\n",
    "uuDist = [[defaultD for t in range(nHDs)] for t in range(nHDs)] #default: too big to capture\n",
    "startTime = time.time()\n",
    "\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %1000 == 0:\n",
    "        print(\"working on tract-tract distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    c = HDcountyNo[t]\n",
    "    for cc in range(nCounties):\n",
    "        if cc in closeCountyList[c] or c == cc:\n",
    "            for tt in countyTractList[cc]:\n",
    "                if tt > t and tt in populatedTractList:\n",
    "                    dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                    uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()\n",
    "print(\"our maxD was\",maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b8d465-4961-4b57-8d63-3f19850c6889",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "2f392acb-ba52-4343-80ae-a58ba0b57366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 9129 . Time = 0\n",
      "working on unit-unit angles for unit 1001 out of 9129 . Time = 550\n",
      "working on unit-unit angles for unit 2003 out of 9129 . Time = 1084\n",
      "working on unit-unit angles for unit 3008 out of 9129 . Time = 1559\n",
      "working on unit-unit angles for unit 4008 out of 9129 . Time = 1985\n",
      "working on unit-unit angles for unit 5008 out of 9129 . Time = 2370\n",
      "working on unit-unit angles for unit 6009 out of 9129 . Time = 2714\n",
      "working on unit-unit angles for unit 7011 out of 9129 . Time = 2982\n",
      "working on unit-unit angles for unit 8016 out of 9129 . Time = 3159\n",
      "working on unit-unit angles for unit 9018 out of 9129 . Time = 3226\n",
      "All done with angles; total elapsed seconds = 3227\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 - updated for CA\n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %1000 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )        \n",
    "    c = HDcountyNo[t]  #this line was previously missing\n",
    "    for cc in range(nCounties):\n",
    "        if cc in closeCountyList[c] or c == cc:\n",
    "            for tt in countyTractList[cc]:\n",
    "                if tt > t and tt in populatedTractList:\n",
    "                    angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "                    uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "#plt.hist(uuAngle)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8f4a3d7-e3eb-409a-92c2-722970909368",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "4cc54eba-ca37-4680-9699-37332133afb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "760350.442 52 39538223.0 52.00000000000001 39538223.0\n",
      "39538223.0 39538223 1.0\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, statePop, statePop/aDP, trueStatePop)\n",
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), r5(np.sum(HDweight))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "dac2348a-060a-40a9-beeb-02ff70ec1aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)\n",
    "#for c in range(nCounties):\n",
    "#    for cc in opt2nbrCtyList[c]:\n",
    "#        plotPoly(countyGeom[cc])\n",
    "#    plotCenter(c,countyGeom[c])\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74e57b0f-7368-4573-b5fa-89df5529f1e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "3873aaa2-0c39-4ac0-99c3-92dd7c18c555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 3471.691 of targetWedgePop\n",
      "I am working on HD number 0 of 9129 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 401 of 9129 HDs.  Elapsed sec = 124\n",
      "I am working on HD number 801 of 9129 HDs.  Elapsed sec = 246\n",
      "I am working on HD number 1201 of 9129 HDs.  Elapsed sec = 372\n",
      "I am working on HD number 1603 of 9129 HDs.  Elapsed sec = 492\n",
      "I am working on HD number 2003 of 9129 HDs.  Elapsed sec = 598\n",
      "I am working on HD number 2405 of 9129 HDs.  Elapsed sec = 717\n",
      "I am working on HD number 2808 of 9129 HDs.  Elapsed sec = 844\n",
      "I am working on HD number 3208 of 9129 HDs.  Elapsed sec = 964\n",
      "I am working on HD number 3608 of 9129 HDs.  Elapsed sec = 1079\n",
      "I am working on HD number 4008 of 9129 HDs.  Elapsed sec = 1216\n",
      "I am working on HD number 4408 of 9129 HDs.  Elapsed sec = 1332\n",
      "I am working on HD number 4808 of 9129 HDs.  Elapsed sec = 1460\n",
      "I am working on HD number 5209 of 9129 HDs.  Elapsed sec = 1598\n",
      "I am working on HD number 5609 of 9129 HDs.  Elapsed sec = 1729\n",
      "I am working on HD number 6009 of 9129 HDs.  Elapsed sec = 1861\n",
      "I am working on HD number 6409 of 9129 HDs.  Elapsed sec = 1986\n",
      "I am working on HD number 6811 of 9129 HDs.  Elapsed sec = 2102\n",
      "I am working on HD number 7212 of 9129 HDs.  Elapsed sec = 2219\n",
      "I am working on HD number 7614 of 9129 HDs.  Elapsed sec = 2341\n",
      "I am working on HD number 8016 of 9129 HDs.  Elapsed sec = 2453\n",
      "I am working on HD number 8416 of 9129 HDs.  Elapsed sec = 2565\n",
      "I am working on HD number 8816 of 9129 HDs.  Elapsed sec = 2682\n",
      "2787.26 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\n",
      "vtd avg and sd usage are 0.99878 0.14015\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"vtd avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "b7f36a0f-a3c6-4b07-aecd-9e010b02918f",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATE = \"07Jun\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "37c238bb-b340-431b-ad11-95e4409d9699",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea, \"countyNo\":countyNo,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_tract_\"+str(int(nDistricts))+\"nW4_\"+DATE+\".csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "075013a3-f3ee-4a44-ac4a-27a7492e027a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./2024state_HD_output/CA9129convexHD2_tract_52nW4_07Jun.csv\n"
     ]
    }
   ],
   "source": [
    "print(\"./\"+outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/CA9129convexHD2_tract_52nW4_07Jun.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 --> 1 - we already have an ensemble of HDpoly's and their weights (from running option 2 above)\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"countyNo\" in shapeDF.columns.values:  #\"pigs\" == \"can fly\":\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "    if len(unassignedList) > 0:\n",
    "        if len(unassignedList) > 20:\n",
    "            print(\"  (too many to plot individually)\")\n",
    "        else:\n",
    "            print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "            for t in unassignedList:\n",
    "                print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "                plotPoly(hdCP[t].buffer(0.1))\n",
    "                plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "                plotPoly(countyGeom[HDcountyNo[t]])\n",
    "                plotPoly(MAP,0.2)\n",
    "                plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "3f0f8434-5f7a-41d1-bd86-07ec230b4aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, let me renormalize the HDweights if there were cut districts\n",
      "our HDweight sum was 0.9999999999997419 but is now 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"now, let me renormalize the HDweights if there were cut districts\")\n",
    "sumWt = np.sum(HDweight)\n",
    "HDweight = [HDweight[t]/sumWt for t in range(nHDs) ]\n",
    "\n",
    "print(\"our HDweight sum was\",sumWt,\"but is now\",np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af22eba0-8e05-43e7-b96b-6aedab016957",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see early states for continuing w/option 1.  NC and later use option 3 ....\n",
    "# ****if this is a cold restart, would need to read in unit topology before below block  **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "8d5f678f-c2df-4ee7-bafe-fd75ba2cb42e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\n"
     ]
    }
   ],
   "source": [
    "#OPTION 3 \n",
    "print(\"lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\")\n",
    "HDtractListString = shapeDF[\"HDtractList\"]\n",
    "HDtractList = [ast.literal_eval(HDtractListString[t]) for t in range(nHDs)]\n",
    "nCCBs = len(CCBlist)\n",
    "CCBpopFrac = [[0. for j in range(nCCBs) ]  for t in range(nHDs)]\n",
    "\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ec034144-b3dc-4b72-9350-6d85cabcb8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\n",
      "working on vtd --> unit conversion for HD 1 last HD had pop 761272\n",
      "working on vtd --> unit conversion for HD 1001 last HD had pop 762580\n",
      "working on vtd --> unit conversion for HD 2001 last HD had pop 757403\n",
      "working on vtd --> unit conversion for HD 3001 last HD had pop 754282\n",
      "working on vtd --> unit conversion for HD 4001 last HD had pop 761606\n",
      "working on vtd --> unit conversion for HD 5001 last HD had pop 757178\n",
      "working on vtd --> unit conversion for HD 6001 last HD had pop 759827\n",
      "working on vtd --> unit conversion for HD 7001 last HD had pop 717930.0\n",
      "working on vtd --> unit conversion for HD 8001 last HD had pop 757352\n",
      "working on vtd --> unit conversion for HD 9001 last HD had pop 759157\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\" )\n",
    "HDunitList = [ list() for t in range(nHDs) ]\n",
    "for t in range(nHDs):\n",
    "    if t %1000 == 1:\n",
    "        print(\"working on vtd --> unit conversion for HD\",t,\"last HD had pop\",r3( np.sum([unitPop[u] for u in HDunitList[t-1]]) ) )\n",
    "    capturedCountyPop = [0. for c in range(nCounties)]\n",
    "    capturedCCBpop = [0. for i in range(nCCBs)]\n",
    "    for v in HDtractList[t]:\n",
    "        c = HDcountyNo[v]\n",
    "        if c in range(nCounties):  #we assigned countyno = -999 to unpopulated tracts\n",
    "            if c in unitCounties:\n",
    "                capturedCountyPop[c] += tractPop[v]\n",
    "            elif c in allFusedCounties:\n",
    "                for i, L in enumerate(CCBlist):\n",
    "                    if c in L:\n",
    "                        capturedCCBpop[i] += tractPop[v]\n",
    "            else:  #this vtd not part of a unit or fused county.  Could be split into fragments\n",
    "                for u in countyUnitList[c]:\n",
    "                    if unitParentVTDno[u] == v :  #we assign all units = vtd fragments from this HDTL-captured vtd\n",
    "                        HDunitList[t].append(u)\n",
    "    for c in unitCounties:\n",
    "        if capturedCountyPop[c] > 0.5 * countyPop[c]:\n",
    "            HDunitList[t].append(allUnits.index(c+0.5))\n",
    "    for i in range(nCCBs):  #don't yet assign in/out CCBs.  We'll do so in below block.  Just update max possible use for each CCB\n",
    "        CCBpopFrac[t][i] = capturedCCBpop[i] / CCBpop[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "1494814c-7b90-425c-b8b1-3a2006bdeb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\n",
      "CCBno 0 with pop 179702.0 would be used 2.504 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 1 with pop 27743.0 would be used 0.859 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 2 with pop 44076.0 would be used 1.145 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 3 with pop 41430.0 would be used 1.878 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if we are not allowed to use multiple CCB's in a single HD 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCBno 0 with pop 179702.0 would be used 2.5037034163118865 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 1 with pop 27743.0 would be used 0.8588421386565893 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 2 with pop 44076.0 would be used 1.14502465120908 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 3 with pop 41430.0 would be used 1.8783062658127367 if we added it to all adjoining districts with no 2-CCB districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\")\n",
    "CCBwouldUse = [0. for i in range(nCCBs)]\n",
    "CCBaddCandidates = [list() for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    jNo = allUnits.index(j+0.25)\n",
    "    ccbNbrSet = set(unitNbrs[jNo])\n",
    "    withCCBpopRat = list()\n",
    "    for t in range(nHDs):\n",
    "        if len(ccbNbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            CCBwouldUse[j] += HDweight[t] * nDistricts\n",
    "            CCBaddCandidates[j].append(t)\n",
    "            withCCBpopRat.append((np.sum([unitPop[u] for u in HDunitList[t]]) + CCBpop[j]) / aDP )\n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",r3(CCBwouldUse[j]),\"if we added it to all adjoining districts\")\n",
    "    print(\"Here is the histogram of relative district pop with these added\")\n",
    "    plt.hist(withCCBpopRat,histtype=\"step\",label=\"CCB \"+str(j))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "avoidMultiCCBuse = int(input(\"enter 1 if we are not allowed to use multiple CCB's in a single HD\"))  #option by state\n",
    "if avoidMultiCCBuse == 1:\n",
    "    for j in range(nCCBs):  #this block -- preventing HDs from picking up multiple CCB's; instead, pick up the closest one  \n",
    "        jNo = allUnits.index(j+0.25)\n",
    "        for jj in range(j+1, nCCBs):\n",
    "            jjNo = allUnits.index(j+0.25)\n",
    "            for t in CCBaddCandidates[j].copy():\n",
    "                if t in CCBaddCandidates[j]:\n",
    "                    if t in CCBaddCandidates[jj].copy():\n",
    "                        dist_j, dist_jj = hdCP[t].distance(unitCP[jNo]), hdCP[t].distance(unitCP[jjNo])\n",
    "                        if dist_j > dist_jj:\n",
    "                            CCBaddCandidates[j].remove(t)  #for MN, don't allow any districts with two CCB's\n",
    "                            CCBwouldUse[j] -= HDweight[t] * nDistricts\n",
    "                        else:\n",
    "                            CCBaddCandidates[jj].remove(t)\n",
    "                            CCBwouldUse[jj] -= HDweight[t] * nDistricts\n",
    "for j in range(nCCBs):    \n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",CCBwouldUse[j],\"if we added it to all adjoining districts with no 2-CCB districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0d38eb0e-b516-4fd4-bb9e-6ca0eecfa772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 0 I reco 1.02, as we may lose a little later 1.01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 0 is 1.01542 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 1 I reco 1.02, as we may lose a little later 1.08\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 1 is 0.85884 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 2 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 2 is 1.02533 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 3 I reco 1.02, as we may lose a little later 1.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 3 is 1.03311 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\")\n",
    "#previously, we went from most-used to least-used CCB vtds until each has unit use\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "CCBuse = [0. for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    minCCBuse = float(input(\"enter target CCBuse for CCB \"+str(j)+\" I reco 1.02, as we may lose a little later\"))\n",
    "    postCCBaddPops = list()\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    #ccbNbrSet = set(unitNbrs[unitNo])\n",
    "    #ccbFrac = [-1.*CCBpopFrac[t][j] for t in range(nHDs) ]  # -1 to go from most- to least-used\n",
    "    #idx = np.argsort(ccbFrac)\n",
    "    preCCBpop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in CCBaddCandidates[j]]\n",
    "    idx = np.argsort(preCCBpop)\n",
    "    i = 0\n",
    "    t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "    while CCBuse[j] < minCCBuse  and i < len(CCBaddCandidates[j]) :  #and CCBpopFrac[t][j] > 0:\n",
    "        t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        HDunitList[t].append(unitNo)\n",
    "        postCCBaddPops.append(np.sum([unitPop[u] for u in HDunitList[t] ]))\n",
    "        i +=1\n",
    "    unitUse[unitNo] = CCBuse[j]\n",
    "    print(\"final unit use of CCB\",j, \"is\",r5(CCBuse[j]),\"; here is a histogram of HDpop using this CCB\")\n",
    "    plt.hist(postCCBaddPops)\n",
    "    plt.axvline(aDP,ls=\"--\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf747d9c-cf6d-420e-9c3c-6723619586ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#end of preliminaries for \"option 3\" - converting HDtractlists to unit lists instead of option 2's geometric probing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26102ca2-79c1-48db-b873-7fa3a1553911",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig unit avg and sd usage are 0.99838 0.13807\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs) ]\n",
    "print(\"Here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t] / aDP for t in range(nHDs) ], bins=20, weights = HDweight, label= \"HDpop / target\" )\n",
    "plt.show()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e4c298-e6cb-4fee-a511-b8f6c3940539",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c4183011-724a-4014-8c94-f9f9382c7e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Did I grossly overuse any units, or skip any live ones?\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above: underused < 0.5; below overused > 1.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Did I grossly overuse any units, or skip any live ones?\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.5 and unitPop[u] > 5:\n",
    "        plotPoly(unitGeom[u])\n",
    "        #print(u,unitPop[u], unitUse[u])\n",
    "        #for c in range(nCounties):\n",
    "        #    if u in countyUnitList[c]:\n",
    "        #        print(u,\"is in county\",c)\n",
    "        #        print(\"its neighbor units are\",unitNbrs[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"above: underused < 0.5; below overused > 1.6\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 1.6:\n",
    "        plotPoly(unitGeom[u],0.3)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 9111 populated HDs out of 9129\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs out of\",nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3919f50e-87f3-4f1d-98ef-9a184cd5d418",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 701 time is now 132\n",
      "working on HD 1402 time is now 245\n",
      "working on HD 2103 time is now 356\n",
      "working on HD 2808 time is now 467\n",
      "working on HD 3508 time is now 580\n",
      "working on HD 4208 time is now 705\n",
      "working on HD 4908 time is now 809\n",
      "working on HD 5609 time is now 919\n",
      "working on HD 6309 time is now 1062\n",
      "working on HD 7011 time is now 1184\n",
      "working on HD 7716 time is now 1288\n",
      "working on HD 8416 time is now 1378\n",
      "working on HD 9118 time is now 1484\n",
      "out of 9111 total HDs, there were 7001.0 contiguous and 643.0 complement-contiguous HDs\n",
      "1877 HDs had both discontiguity problems, while enclave-only = 6591 and discontig only= 233\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 2110 6591\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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vvvnGqbZz587mz82bN5ckFRQUSJJycnJ000031fi4H330kW666Sa1aNFCgYGBuvfee/XTTz+Zbw/deuut8vT01HvvvSdJ+vvf/y6bzabY2FhJ0u7du3XgwAEFBgaaPYeEhOj06dNn9V0f7Hb7WY+rr8Ds27dPXbp0kb+/vzner18/VVVVOb3N1qVLF/n5+TnNUVxcrMOHD9d7v5cKi3gBAC5RvU5j7dq1atGihdOYt7e302NPT0/zZzc3N0ln1s9Ikq+vb42P+d133+m2227TQw89pOeee04hISH69NNPNWLECJWVlcnPz09eXl668847tWzZMg0bNkzLli3TXXfdpSZNmph99+jRQ0uXLj1r/iuuuKLGveDC1PoKzJEjR3TPPfcoNDRUvr6+6tSpk3bt2mWOG4ahtLQ0NW/eXL6+voqNjdXXX3/tNMexY8eUkJAgm82m4OBgjRgxwmnBkSR98cUXuu666+Tj46OoqChNmzatjqcIALgcxcTEyNvbW4cOHVLbtm2dtqiomt/F1LlzZ2VkZNSoNjs7W1VVVZoxY4b69u2rdu3a6ejRo2fVJSQk6MMPP9SePXu0ceNGJSQkmGPdu3fX119/rbCwsLP6DgoKqnHfNbVt27azHnfs2FGS1LFjR+3evVslJSXm+JYtW+Tu7q727dub+3bv3q1Tp045zREQEFCrv+fLTa0CzPHjx9WvXz95enrqgw8+0N69ezVjxgw1bdrUrJk2bZrmzJmjhQsXavv27fL391dcXJxOnz5t1iQkJGjPnj3asGGD1qxZo82bN2vUqFHmuMPh0MCBA9W6dWtlZ2frxRdf1KRJk/Tqq6/WwykDAC4HgYGBevzxxzV27FgtXrxY33zzjT777DO9/PLLWrx4cY3nmThxot566y1NnDhR+/btU25urv7617+es7Zt27YqLy/Xyy+/rG+//VZvvvmmFi5ceFbd9ddfr4iICCUkJCg6Olp9+vQxxxISEtSsWTPdfvvt+uSTT3Tw4EFt2rRJjzzyiL7//vvz9nngwAHl5OQoLy9Pp06dUk5OjnJycn5z4e/KlSv1xhtv6KuvvtLEiRO1Y8cOc5FuQkKCfHx8lJiYqC+//FIff/yxxowZo3vvvVfh4eHmHGVlZRoxYoT27t2rf/7zn5o4caKSk5Pl7m7hlSS1WXTzxBNPGNdee+15x6uqqoyIiAjjxRdfNPcVFhYa3t7exltvvWUYhmHs3bvXkGTs3LnTrPnggw8MNzc348iRI4ZhGMb8+fONpk2bGqWlpU7Hbt++fY17ZRHvz1ixZwA19puLeHevOPPzxd52r6j1/2uqqqqMWbNmGe3btzc8PT2NK664woiLizMyMzMNw/i/RbzHjx83n/P5558bkoyDBw+a+/7+978bXbt2Nby8vIxmzZoZQ4YMMcd+uYj3pZdeMpo3b274+voacXFxxpIlS846hmEYxvjx4w1JRlpa2ll9//DDD8bw4cONZs2aGd7e3kabNm2MkSNH/uprTv/+/Q1JZ20/P49fkmTMmzfPuPnmmw1vb2/jyiuvNFasWOFU88UXXxgDBgwwfHx8jJCQEGPkyJHGiRMnzPHExETj9ttvN9LS0ozQ0FAjICDAGDlypHH69OnzHvdiq49FvG6GYRg1DTsxMTGKi4vT999/r8zMTLVo0UIPP/ywRo4cKUn69ttvddVVV+nzzz9X165dzef1799fXbt21ezZs/XGG2/oscce0/Hjx83xiooK+fj4aOXKlbrjjjs0fPhwORwOrV692qz5+OOPdeONN+rYsWNOV3yqlZaWqrS01HzscDgUFRWloqIi2Wy2mp7ibzuaI73aXxqVeeZzD6zAij0DqLHTp0/r4MGDio6Olo+Pz/8N8Em8lufm5qZVq1Zp8ODBdZ7jvvvuU2FhodNrqqud99+szrx+BwUF/ebrd60W8X777bdasGCBUlNT9Ze//EU7d+7UI488Ii8vLyUmJiovL0+SnC5bVT+uHsvLy1NYWJhzE02aKCQkxKkmOjr6rDmqx84VYKZOnWr9e9oBoD4FR50JE438u5DQMNUqwFRVValnz556/vnnJUndunXTl19+qYULFyoxMfGiNFhTEyZMUGpqqvm4+goMADRqwVEECjRItQowzZs3V0xMjNO+jh076u9//7skKSIiQpKUn59v3qdf/bj6LaWIiAjz3v1qFRUVOnbsmPn8iIgI5efnO9VUP66u+SVvb++zbrsDAMDKarHK47zS09MvvJHLUK2WH/fr1++s75/46quv1Lp1a0lSdHS0IiIinG5nczgc2r59u/lBPHa7XYWFhcrOzjZrNm7cqKqqKnOVt91u1+bNm1VeXm7WbNiwQe3btz/n20cAAKBxqVWAGTt2rLZt26bnn39eBw4c0LJly/Tqq68qKSlJ0pnFRikpKXr22Wf13nvvKTc3V8OHD1dkZKS5AKljx4665ZZbNHLkSO3YsUNbtmxRcnKyhg0bpsjISEnSn/70J3l5eWnEiBHas2ePVqxYodmzZzu9RQQAABqvWr2F1KtXL61atUoTJkzQlClTFB0drVmzZjl9wM/48eNVUlKiUaNGqbCwUNdee60+/PBDp1XGS5cuVXJysm666Sa5u7tr6NChmjNnjjkeFBSk9evXKykpST169FCzZs2Ulpbm9FkxAABn9fF2A3Ap1Me/1VrdRm0lNb0Nq9aseEuyFXsGUGOVlZX66quvFBYWptDQUFe3A/ymn376SQUFBWrXrp08PDycxi7KbdQAgMuPh4eHgoODzRsk/Pz8zO8LAi4nhmHo5MmTKigoUHBw8FnhpTYIMADQAFTfofnLuzyBy1FwcPB57yquKQIMADQAbm5uat68ucLCwpzu4AQuN56enhd05aUaAQYAGhAPD496eXEALncW/hpKAADQWBFgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5dQqwEyaNElubm5OW4cOHczx06dPKykpSaGhoQoICNDQoUOVn5/vNMehQ4cUHx8vPz8/hYWFady4caqoqHCq2bRpk7p37y5vb2+1bdtW6enpdT9DAADQ4NT6CszVV1+tH374wdw+/fRTc2zs2LF6//33tXLlSmVmZuro0aMaMmSIOV5ZWan4+HiVlZVp69atWrx4sdLT05WWlmbWHDx4UPHx8RowYIBycnKUkpKiBx98UOvWrbvAUwUAAA1Fk1o/oUkTRUREnLW/qKhIr7/+upYtW6Ybb7xRkrRo0SJ17NhR27ZtU9++fbV+/Xrt3btXH330kcLDw9W1a1c988wzeuKJJzRp0iR5eXlp4cKFio6O1owZMyRJHTt21KeffqqZM2cqLi7uAk8XAAA0BLW+AvP1118rMjJSbdq0UUJCgg4dOiRJys7OVnl5uWJjY83aDh06qFWrVsrKypIkZWVlqVOnTgoPDzdr4uLi5HA4tGfPHrPm53NU11TPcT6lpaVyOBxOGwAAaJhqFWD69Omj9PR0ffjhh1qwYIEOHjyo6667TidOnFBeXp68vLwUHBzs9Jzw8HDl5eVJkvLy8pzCS/V49div1TgcDp06deq8vU2dOlVBQUHmFhUVVZtTAwAAFlKrt5AGDRpk/ty5c2f16dNHrVu31ttvvy1fX996b642JkyYoNTUVPOxw+EgxAAA0EBd0G3UwcHBateunQ4cOKCIiAiVlZWpsLDQqSY/P99cMxMREXHWXUnVj3+rxmaz/WpI8vb2ls1mc9oAAEDDdEEBpri4WN98842aN2+uHj16yNPTUxkZGeb4/v37dejQIdntdkmS3W5Xbm6uCgoKzJoNGzbIZrMpJibGrPn5HNU11XMAAADUKsA8/vjjyszM1HfffaetW7fqjjvukIeHh+6++24FBQVpxIgRSk1N1ccff6zs7Gzdf//9stvt6tu3ryRp4MCBiomJ0b333qvdu3dr3bp1euqpp5SUlCRvb29J0ujRo/Xtt99q/Pjx+te//qX58+fr7bff1tixY+v/7AEAgCXVag3M999/r7vvvls//fSTrrjiCl177bXatm2brrjiCknSzJkz5e7urqFDh6q0tFRxcXGaP3+++XwPDw+tWbNGDz30kOx2u/z9/ZWYmKgpU6aYNdHR0Vq7dq3Gjh2r2bNnq2XLlnrttde4hRoAAJjcDMMwXN3ExeBwOBQUFKSioqL6XQ9zNEd6tb80KlOK7Fp/815MVuwZANAo1fT1m+9CAgAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlnNBAeaFF16Qm5ubUlJSzH2nT59WUlKSQkNDFRAQoKFDhyo/P9/peYcOHVJ8fLz8/PwUFhamcePGqaKiwqlm06ZN6t69u7y9vdW2bVulp6dfSKsAAKABqXOA2blzp1555RV17tzZaf/YsWP1/vvva+XKlcrMzNTRo0c1ZMgQc7yyslLx8fEqKyvT1q1btXjxYqWnpystLc2sOXjwoOLj4zVgwADl5OQoJSVFDz74oNatW1fXdgEAQANSpwBTXFyshIQE/e1vf1PTpk3N/UVFRXr99df10ksv6cYbb1SPHj20aNEibd26Vdu2bZMkrV+/Xnv37tX//u//qmvXrho0aJCeeeYZzZs3T2VlZZKkhQsXKjo6WjNmzFDHjh2VnJysO++8UzNnzqyHUwYAAFZXpwCTlJSk+Ph4xcbGOu3Pzs5WeXm50/4OHTqoVatWysrKkiRlZWWpU6dOCg8PN2vi4uLkcDi0Z88es+aXc8fFxZlznEtpaakcDofTBgAAGqYmtX3C8uXL9dlnn2nnzp1njeXl5cnLy0vBwcFO+8PDw5WXl2fW/Dy8VI9Xj/1ajcPh0KlTp+Tr63vWsadOnarJkyfX9nQAAIAF1eoKzOHDh/Xoo49q6dKl8vHxuVg91cmECRNUVFRkbocPH3Z1SwAA4CKpVYDJzs5WQUGBunfvriZNmqhJkybKzMzUnDlz1KRJE4WHh6usrEyFhYVOz8vPz1dERIQkKSIi4qy7kqof/1aNzWY759UXSfL29pbNZnPaAABAw1SrAHPTTTcpNzdXOTk55tazZ08lJCSYP3t6eiojI8N8zv79+3Xo0CHZ7XZJkt1uV25urgoKCsyaDRs2yGazKSYmxqz5+RzVNdVzAACAxq1Wa2ACAwN1zTXXOO3z9/dXaGiouX/EiBFKTU1VSEiIbDabxowZI7vdrr59+0qSBg4cqJiYGN17772aNm2a8vLy9NRTTykpKUne3t6SpNGjR2vu3LkaP368HnjgAW3cuFFvv/221q5dWx/nDAAALK7Wi3h/y8yZM+Xu7q6hQ4eqtLRUcXFxmj9/vjnu4eGhNWvW6KGHHpLdbpe/v78SExM1ZcoUsyY6Olpr167V2LFjNXv2bLVs2VKvvfaa4uLi6rtdAABgQW6GYRiubuJicDgcCgoKUlFRUf2uhzmaI73aXxqVKUV2rb95LyYr9gwAaJRq+vrNdyEBAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLqVWAWbBggTp37iybzSabzSa73a4PPvjAHD99+rSSkpIUGhqqgIAADR06VPn5+U5zHDp0SPHx8fLz81NYWJjGjRuniooKp5pNmzape/fu8vb2Vtu2bZWenl73MwQAAA1OrQJMy5Yt9cILLyg7O1u7du3SjTfeqNtvv1179uyRJI0dO1bvv/++Vq5cqczMTB09elRDhgwxn19ZWan4+HiVlZVp69atWrx4sdLT05WWlmbWHDx4UPHx8RowYIBycnKUkpKiBx98UOvWraunUwYAAJZnXKCmTZsar732mlFYWGh4enoaK1euNMf27dtnSDKysrIMwzCMf/7zn4a7u7uRl5dn1ixYsMCw2WxGaWmpYRiGMX78eOPqq692OsZdd91lxMXF1aqvoqIiQ5JRVFRU11M7tyOfG8ZE25k/rcKKPQMAGqWavn7XeQ1MZWWlli9frpKSEtntdmVnZ6u8vFyxsbFmTYcOHdSqVStlZWVJkrKystSpUyeFh4ebNXFxcXI4HOZVnKysLKc5qmuq5zif0tJSORwOpw0AADRMtQ4wubm5CggIkLe3t0aPHq1Vq1YpJiZGeXl58vLyUnBwsFN9eHi48vLyJEl5eXlO4aV6vHrs12ocDodOnTp13r6mTp2qoKAgc4uKiqrtqQEAAIuodYBp3769cnJytH37dj300ENKTEzU3r17L0ZvtTJhwgQVFRWZ2+HDh13dEgAAuEia1PYJXl5eatu2rSSpR48e2rlzp2bPnq277rpLZWVlKiwsdLoKk5+fr4iICElSRESEduzY4TRf9V1KP6/55Z1L+fn5stls8vX1PW9f3t7e8vb2ru3pAAAAC7rgz4GpqqpSaWmpevToIU9PT2VkZJhj+/fv16FDh2S32yVJdrtdubm5KigoMGs2bNggm82mmJgYs+bnc1TXVM8BAABQqyswEyZM0KBBg9SqVSudOHFCy5Yt06ZNm7Ru3ToFBQVpxIgRSk1NVUhIiGw2m8aMGSO73a6+fftKkgYOHKiYmBjde++9mjZtmvLy8vTUU08pKSnJvHoyevRozZ07V+PHj9cDDzygjRs36u2339batWvr/+wBAIAl1SrAFBQUaPjw4frhhx8UFBSkzp07a926dbr55pslSTNnzpS7u7uGDh2q0tJSxcXFaf78+ebzPTw8tGbNGj300EOy2+3y9/dXYmKipkyZYtZER0dr7dq1Gjt2rGbPnq2WLVvqtddeU1xcXD2dMgAAsDo3wzAMVzdxMTgcDgUFBamoqEg2m63+Jj6aI73aXxqVKUV2rb95LyYr9gwAaJRq+vrNdyEBAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLqVWAmTp1qnr16qXAwECFhYVp8ODB2r9/v1PN6dOnlZSUpNDQUAUEBGjo0KHKz893qjl06JDi4+Pl5+ensLAwjRs3ThUVFU41mzZtUvfu3eXt7a22bdsqPT29bmcIAAAanFoFmMzMTCUlJWnbtm3asGGDysvLNXDgQJWUlJg1Y8eO1fvvv6+VK1cqMzNTR48e1ZAhQ8zxyspKxcfHq6ysTFu3btXixYuVnp6utLQ0s+bgwYOKj4/XgAEDlJOTo5SUFD344INat25dPZwyAACwPOMCFBQUGJKMzMxMwzAMo7Cw0PD09DRWrlxp1uzbt8+QZGRlZRmGYRj//Oc/DXd3dyMvL8+sWbBggWGz2YzS0lLDMAxj/PjxxtVXX+10rLvuusuIi4urcW9FRUWGJKOoqKjO53dORz43jIm2M39ahRV7BgA0SjV9/b6gNTBFRUWSpJCQEElSdna2ysvLFRsba9Z06NBBrVq1UlZWliQpKytLnTp1Unh4uFkTFxcnh8OhPXv2mDU/n6O6pnqOcyktLZXD4XDaAABAw1TnAFNVVaWUlBT169dP11xzjSQpLy9PXl5eCg4OdqoNDw9XXl6eWfPz8FI9Xj32azUOh0OnTp06Zz9Tp05VUFCQuUVFRdX11AAAwGWuzgEmKSlJX375pZYvX16f/dTZhAkTVFRUZG6HDx92dUsAAOAiaVKXJyUnJ2vNmjXavHmzWrZsae6PiIhQWVmZCgsLna7C5OfnKyIiwqzZsWOH03zVdyn9vOaXdy7l5+fLZrPJ19f3nD15e3vL29u7LqcDAAAsplZXYAzDUHJyslatWqWNGzcqOjraabxHjx7y9PRURkaGuW///v06dOiQ7Ha7JMlutys3N1cFBQVmzYYNG2Sz2RQTE2PW/HyO6prqOQAAQONWqyswSUlJWrZsmf7xj38oMDDQXLMSFBQkX19fBQUFacSIEUpNTVVISIhsNpvGjBkju92uvn37SpIGDhyomJgY3XvvvZo2bZry8vL01FNPKSkpybyCMnr0aM2dO1fjx4/XAw88oI0bN+rtt9/W2rVr6/n0AQCAFdXqCsyCBQtUVFSkG264Qc2bNze3FStWmDUzZ87UbbfdpqFDh+r6669XRESE3n33XXPcw8NDa9askYeHh+x2u+655x4NHz5cU6ZMMWuio6O1du1abdiwQV26dNGMGTP02muvKS4urh5OGQAAWJ2bYRiGq5u4GBwOh4KCglRUVCSbzVZ/Ex/NkV7tL43KlCK71t+8F5MVewYANEo1ff3mu5AAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlNHF1A1Z14MdinTaKJElN/b3UItjXxR0BANB4EGBqqaC4VGGSHl2eoz3/P8D4enroo8f6E2IAALhECDC15DhVrjBJjw9sryva9daBgmKlrMjR8ZIyAgwAAJcIAaaOokJ81bZFkKvbAACgUWIRLwAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsJxaB5jNmzfr97//vSIjI+Xm5qbVq1c7jRuGobS0NDVv3ly+vr6KjY3V119/7VRz7NgxJSQkyGazKTg4WCNGjFBxcbFTzRdffKHrrrtOPj4+ioqK0rRp02p/dgAAoEGqdYApKSlRly5dNG/evHOOT5s2TXPmzNHChQu1fft2+fv7Ky4uTqdPnzZrEhIStGfPHm3YsEFr1qzR5s2bNWrUKHPc4XBo4MCBat26tbKzs/Xiiy9q0qRJevXVV+twigAAoKGp9VcJDBo0SIMGDTrnmGEYmjVrlp566indfvvtkqQlS5YoPDxcq1ev1rBhw7Rv3z59+OGH2rlzp3r27ClJevnll3Xrrbdq+vTpioyM1NKlS1VWVqY33nhDXl5euvrqq5WTk6OXXnrJKegAAIDGqV7XwBw8eFB5eXmKjY019wUFBalPnz7KysqSJGVlZSk4ONgML5IUGxsrd3d3bd++3ay5/vrr5eXlZdbExcVp//79On78+DmPXVpaKofD4bQBAICGqV4DTF5eniQpPDzcaX94eLg5lpeXp7CwMKfxJk2aKCQkxKnmXHP8/Bi/NHXqVAUFBZlbVFTUhZ8QAAC4LDWYu5AmTJigoqIiczt8+LCrWwIAABdJvQaYiIgISVJ+fr7T/vz8fHMsIiJCBQUFTuMVFRU6duyYU8255vj5MX7J29tbNpvNaQMAAA1TvQaY6OhoRUREKCMjw9zncDi0fft22e12SZLdbldhYaGys7PNmo0bN6qqqkp9+vQxazZv3qzy8nKzZsOGDWrfvr2aNm1any0DAAALqnWAKS4uVk5OjnJyciSdWbibk5OjQ4cOyc3NTSkpKXr22Wf13nvvKTc3V8OHD1dkZKQGDx4sSerYsaNuueUWjRw5Ujt27NCWLVuUnJysYcOGKTIyUpL0pz/9SV5eXhoxYoT27NmjFStWaPbs2UpNTa23EwcAANZV69uod+3apQEDBpiPq0NFYmKi0tPTNX78eJWUlGjUqFEqLCzUtddeqw8//FA+Pj7mc5YuXark5GTddNNNcnd319ChQzVnzhxzPCgoSOvXr1dSUpJ69OihZs2aKS0tjVuoAQCApDoEmBtuuEGGYZx33M3NTVOmTNGUKVPOWxMSEqJly5b96nE6d+6sTz75pLbtAQCARqDB3IUEAAAaDwIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwnCaubqChOFBQbP7c1N9LLYJ9z1t7pPCUjpeU1bgeAAA4I8BcoKb+XvL19FDKihxzn6+nhz56rP85Q8mRwlOKnZGpU+WVNaoHAABnI8BcoBbBvvrosf7mFZUDBcVKWZGjnQeP6XhYwFn1BwqKdaq8UrPu6qq2YQFm/fGSMgIMAAA1RICpBy2Cfc3wca4rMr/k6+mhXtEhBBYAAOqIAFPPfnlF5lzOtealNmtoAABo7AgwF8HPr8j8ltquoQEAAJd5gJk3b55efPFF5eXlqUuXLnr55ZfVu3dvV7dVr863hubX1sTU+S6m/3xVLz1fUn6hUnCUq7sAAFxmLtsAs2LFCqWmpmrhwoXq06ePZs2apbi4OO3fv19hYWGubq9e/dYVm58Hlp9KyjT6zeza3cXkFyp5+knvjqzXvi8JTz8paQchBgDg5LINMC+99JJGjhyp+++/X5K0cOFCrV27Vm+88Yb+/Oc/u7i7i696Tcz5AsviB3or1N/rnHc9nXVFJjjqTAg4+dMlPYcL9p+vzoSukz8RYAAATi7LAFNWVqbs7GxNmDDB3Ofu7q7Y2FhlZWWd8zmlpaUqLS01HxcVFUmSHA5HvfZ2orhEjlLjzJ/1PLckNak8La+q03pkyVZzn4+nu+YP66YQP09JUrCflyKDvf9/fZNz1r+XfK0ifx5i3IOkgKB67/eiOlEslRrSdzlnfgYAXD4CwqXA8Hqftvq11TCMXy80LkNHjhwxJBlbt2512j9u3Dijd+/e53zOxIkTDUlsbGxsbGxsDWA7fPjwr2aFy/IKTF1MmDBBqamp5uOqqiodO3ZMoaGhcnNzq7fjOBwORUVF6fDhw7LZbPU2L2qH34Pr8TtwPX4HrsfvoP4ZhqETJ04oMjLyV+suywDTrFkzeXh4KD8/32l/fn6+IiIizvkcb29veXt7O+0LDg6+WC3KZrPxj/UywO/B9fgduB6/A9fjd1C/goKCfrPmsvw2ai8vL/Xo0UMZGRnmvqqqKmVkZMhut7uwMwAAcDm4LK/ASFJqaqoSExPVs2dP9e7dW7NmzVJJSYl5VxIAAGi8LtsAc9ddd+nHH39UWlqa8vLy1LVrV3344YcKD6//Fc+14e3trYkTJ571dhUuLX4PrsfvwPX4HbgevwPXcTOM37pPCQAA4PJyWa6BAQAA+DUEGAAAYDkEGAAAYDkEGAAAYDkEmFqaN2+errzySvn4+KhPnz7asWOHq1tqVDZv3qzf//73ioyMlJubm1avXu3qlhqVqVOnqlevXgoMDFRYWJgGDx6s/fv3u7qtRmXBggXq3Lmz+cFpdrtdH3zwgavbatReeOEFubm5KSUlxdWtNCoEmFpYsWKFUlNTNXHiRH322Wfq0qWL4uLiVFBQ4OrWGo2SkhJ16dJF8+bNc3UrjVJmZqaSkpK0bds2bdiwQeXl5Ro4cKBKSkpc3Vqj0bJlS73wwgvKzs7Wrl27dOONN+r222/Xnj17XN1ao7Rz50698sor6ty5s6tbaXS4jboW+vTpo169emnu3LmSznw6cFRUlMaMGaM///nPLu6u8XFzc9OqVas0ePBgV7fSaP34448KCwtTZmamrr/+ele302iFhIToxRdf1IgRI1zdSqNSXFys7t27a/78+Xr22WfVtWtXzZo1y9VtNRpcgamhsrIyZWdnKzY21tzn7u6u2NhYZWVlubAzwHWKiooknXkBxaVXWVmp5cuXq6SkhK9ZcYGkpCTFx8c7vS7g0rlsP4n3cvOf//xHlZWVZ30ScHh4uP71r3+5qCvAdaqqqpSSkqJ+/frpmmuucXU7jUpubq7sdrtOnz6tgIAArVq1SjExMa5uq1FZvny5PvvsM+3cudPVrTRaBBgAdZKUlKQvv/xSn376qatbaXTat2+vnJwcFRUV6Z133lFiYqIyMzMJMZfI4cOH9eijj2rDhg3y8fFxdTuNFgGmhpo1ayYPDw/l5+c77c/Pz1dERISLugJcIzk5WWvWrNHmzZvVsmVLV7fT6Hh5ealt27aSpB49emjnzp2aPXu2XnnlFRd31jhkZ2eroKBA3bt3N/dVVlZq8+bNmjt3rkpLS+Xh4eHCDhsH1sDUkJeXl3r06KGMjAxzX1VVlTIyMnjvGY2GYRhKTk7WqlWrtHHjRkVHR7u6JejM/4tKS0td3UajcdNNNyk3N1c5OTnm1rNnTyUkJCgnJ4fwcolwBaYWUlNTlZiYqJ49e6p3796aNWuWSkpKdP/997u6tUajuLhYBw4cMB8fPHhQOTk5CgkJUatWrVzYWeOQlJSkZcuW6R//+IcCAwOVl5cnSQoKCpKvr6+Lu2scJkyYoEGDBqlVq1Y6ceKEli1bpk2bNmndunWubq3RCAwMPGvdl7+/v0JDQ1kPdgkRYGrhrrvu0o8//qi0tDTl5eWpa9eu+vDDD89a2IuLZ9euXRowYID5ODU1VZKUmJio9PR0F3XVeCxYsECSdMMNNzjtX7Roke67775L31AjVFBQoOHDh+uHH35QUFCQOnfurHXr1unmm292dWvAJcXnwAAAAMthDQwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAAKixzZs36/e//70iIyPl5uam1atX13oOwzA0ffp0tWvXTt7e3mrRooWee+65Ws3BJ/ECAIAaKykpUZcuXfTAAw9oyJAhdZrj0Ucf1fr16zV9+nR16tRJx44d07Fjx2o1B5/ECwAA6sTNzU2rVq3S4MGDzX2lpaV68skn9dZbb6mwsFDXXHON/vrXv5pfQbJv3z517txZX375pdq3b1/nY/MWEgAAqDfJycnKysrS8uXL9cUXX+gPf/iDbrnlFn399deSpPfff19t2rTRmjVrFB0drSuvvFIPPvhgra/AEGAAAEC9OHTokBYtWqSVK1fquuuu01VXXaXHH39c1157rRYtWiRJ+vbbb/Xvf/9bK1eu1JIlS5Senq7s7GzdeeedtToWa2AAAEC9yM3NVWVlpdq1a+e0v7S0VKGhoZKkqqoqlZaWasmSJWbd66+/rh49emj//v01fluJAAMAAOpFcXGxPDw8lJ2dLQ8PD6exgIAASVLz5s3VpEkTp5DTsWNHSWeu4BBgAADAJdWtWzdVVlaqoKBA11133Tlr+vXrp4qKCn3zzTe66qqrJElfffWVJKl169Y1PhZ3IQEAgBorLi7WgQMHJJ0JLC+99JIGDBigkJAQtWrVSvfcc4+2bNmiGTNmqFu3bvrxxx+VkZGhzp07Kz4+XlVVVerVq5cCAgI0a9YsVVVVKSkpSTabTevXr69xHwQYAABQY5s2bdKAAQPO2p+YmKj09HSVl5fr2Wef1ZIlS3TkyBE1a9ZMffv21eTJk9WpUydJ0tGjRzVmzBitX79e/v7+GjRokGbMmKGQkJAa90GAAQAAlsNt1AAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHL+H1PYZoyrhkYdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 722332 district pop vs 760350 target\n",
      "we'll also trim any HD with total islands' pop <= 15207\n",
      "working on HD 15\n",
      "working on HD 857\n",
      "working on HD 1598\n",
      "working on HD 2429\n",
      "working on HD 3630\n",
      "working on HD 4515\n",
      "working on HD 5182\n",
      "working on HD 5928\n",
      "working on HD 6881\n",
      "working on HD 7836\n",
      "working on HD 8828\n",
      "Out of 2110 discontig HDs 2110 had discontig HDs.\n",
      "Of these, 1174 won't be under 722332 after all minor discontigys were shed, while 936 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 15\n",
      "reclassifying HD 2077\n",
      "reclassifying HD 4515\n",
      "reclassifying HD 6276\n",
      "reclassifying HD 8828\n",
      "There are 936 HDs still with both discontinuity problems\n",
      "Additionally, 1054 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 0 0 seconds elapsed\n",
      "working on enclave-generating HD 699 1 seconds elapsed\n",
      "working on enclave-generating HD 1394 2 seconds elapsed\n",
      "working on enclave-generating HD 2079 3 seconds elapsed\n",
      "working on enclave-generating HD 2758 4 seconds elapsed\n",
      "working on enclave-generating HD 3380 5 seconds elapsed\n",
      "working on enclave-generating HD 4119 6 seconds elapsed\n",
      "working on enclave-generating HD 4782 8 seconds elapsed\n",
      "working on enclave-generating HD 5535 9 seconds elapsed\n",
      "working on enclave-generating HD 6293 10 seconds elapsed\n",
      "working on enclave-generating HD 6945 11 seconds elapsed\n",
      "working on enclave-generating HD 7607 12 seconds elapsed\n",
      "working on enclave-generating HD 8242 13 seconds elapsed\n",
      "working on enclave-generating HD 9001 15 seconds elapsed\n",
      "working on enclave-generating HD 3683 16 seconds elapsed\n",
      "working on enclave-generating HD 7913 17 seconds elapsed\n",
      "Out of 7645 HDpolys that generated enclaves, 0 had contiguous adjrs, while 3952 neighbored a state boundary while 3693 had only internal enclaves\n"
     ]
    }
   ],
   "source": [
    "print(\"Classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t,int(time.time()-startTime),\"seconds elapsed\")\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 798367 district pop vs 760350 target\n",
      "working on enclave-y HD 2 . We have evaluated 0 of 7645 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 865 . We have evaluated 200 of 7645 enclavy HDs.Time is now 40\n",
      "working on enclave-y HD 1547 . We have evaluated 400 of 7645 enclavy HDs.Time is now 74\n",
      "working on enclave-y HD 2054 . We have evaluated 600 of 7645 enclavy HDs.Time is now 102\n",
      "working on enclave-y HD 2590 . We have evaluated 800 of 7645 enclavy HDs.Time is now 141\n",
      "working on enclave-y HD 2928 . We have evaluated 1000 of 7645 enclavy HDs.Time is now 172\n",
      "working on enclave-y HD 3345 . We have evaluated 1200 of 7645 enclavy HDs.Time is now 197\n",
      "working on enclave-y HD 4013 . We have evaluated 1400 of 7645 enclavy HDs.Time is now 235\n",
      "working on enclave-y HD 4514 . We have evaluated 1600 of 7645 enclavy HDs.Time is now 270\n",
      "working on enclave-y HD 5167 . We have evaluated 1800 of 7645 enclavy HDs.Time is now 304\n",
      "working on enclave-y HD 5606 . We have evaluated 2000 of 7645 enclavy HDs.Time is now 333\n",
      "working on enclave-y HD 6138 . We have evaluated 2200 of 7645 enclavy HDs.Time is now 377\n",
      "working on enclave-y HD 6659 . We have evaluated 2400 of 7645 enclavy HDs.Time is now 409\n",
      "working on enclave-y HD 7075 . We have evaluated 2600 of 7645 enclavy HDs.Time is now 445\n",
      "working on enclave-y HD 7509 . We have evaluated 2800 of 7645 enclavy HDs.Time is now 474\n",
      "working on enclave-y HD 7977 . We have evaluated 3000 of 7645 enclavy HDs.Time is now 496\n",
      "working on enclave-y HD 8490 . We have evaluated 3200 of 7645 enclavy HDs.Time is now 516\n",
      "working on enclave-y HD 1272 . We have evaluated 3400 of 7645 enclavy HDs.Time is now 554\n",
      "working on enclave-y HD 6419 . We have evaluated 3600 of 7645 enclavy HDs.Time is now 601\n",
      "working on enclave-y HD 213 . We have evaluated 3800 of 7645 enclavy HDs.Time is now 645\n",
      "working on enclave-y HD 661 . We have evaluated 4000 of 7645 enclavy HDs.Time is now 835\n",
      "working on enclave-y HD 1041 . We have evaluated 4200 of 7645 enclavy HDs.Time is now 952\n",
      "working on enclave-y HD 1590 . We have evaluated 4400 of 7645 enclavy HDs.Time is now 1119\n",
      "working on enclave-y HD 2141 . We have evaluated 4600 of 7645 enclavy HDs.Time is now 1220\n",
      "working on enclave-y HD 2808 . We have evaluated 4800 of 7645 enclavy HDs.Time is now 1366\n",
      "working on enclave-y HD 3495 . We have evaluated 5000 of 7645 enclavy HDs.Time is now 1458\n",
      "working on enclave-y HD 3954 . We have evaluated 5200 of 7645 enclavy HDs.Time is now 1524\n",
      "working on enclave-y HD 4539 . We have evaluated 5400 of 7645 enclavy HDs.Time is now 1601\n",
      "working on enclave-y HD 5122 . We have evaluated 5600 of 7645 enclavy HDs.Time is now 1702\n",
      "working on enclave-y HD 5851 . We have evaluated 5800 of 7645 enclavy HDs.Time is now 1829\n",
      "working on enclave-y HD 6470 . We have evaluated 6000 of 7645 enclavy HDs.Time is now 1894\n",
      "working on enclave-y HD 7152 . We have evaluated 6200 of 7645 enclavy HDs.Time is now 2002\n",
      "working on enclave-y HD 7787 . We have evaluated 6400 of 7645 enclavy HDs.Time is now 2056\n",
      "working on enclave-y HD 8335 . We have evaluated 6600 of 7645 enclavy HDs.Time is now 2140\n",
      "working on enclave-y HD 8767 . We have evaluated 6800 of 7645 enclavy HDs.Time is now 2234\n",
      "working on enclave-y HD 970 . We have evaluated 7000 of 7645 enclavy HDs.Time is now 2613\n",
      "working on enclave-y HD 3715 . We have evaluated 7200 of 7645 enclavy HDs.Time is now 2847\n",
      "working on enclave-y HD 5869 . We have evaluated 7400 of 7645 enclavy HDs.Time is now 2988\n",
      "working on enclave-y HD 8743 . We have evaluated 7600 of 7645 enclavy HDs.Time is now 3127\n",
      "Out of 7645 HDs with enclaves 7645 had enclaves.\n",
      "Of these, 7482 wouldn't be over 798367 if all enclaves filled, while 163 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%200 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3693 3952 1877 730\n",
      "158 163 7482\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))\n",
    "#610 3379 2187 1954\n",
    "#80 80 3909  for original MN option3 (assigned CCBs by captured area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00278 0.13786\n",
      "CCB cluster 0 = unit 9020 with pop 179702.0 now has use of 1.0154212545161716\n",
      "CCB cluster 1 = unit 9021 with pop 27743.0 now has use of 0.8588421386565893\n",
      "CCB cluster 2 = unit 9022 with pop 44076.0 now has use of 1.0253258979292468\n",
      "CCB cluster 3 = unit 9023 with pop 41430.0 now has use of 1.0699684707630983\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.axvline(aDP, ls=\"--\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 163 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the barredJettisonSet (cannot be shed in below MUSTFREE code) is {9020, 9021, 9022, 9023}\n",
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 801\n",
      "working on avg unit-to-HDcp dist for HD 1603\n",
      "working on avg unit-to-HDcp dist for HD 2405\n",
      "working on avg unit-to-HDcp dist for HD 3208\n",
      "working on avg unit-to-HDcp dist for HD 4008\n",
      "working on avg unit-to-HDcp dist for HD 4808\n",
      "working on avg unit-to-HDcp dist for HD 5609\n",
      "working on avg unit-to-HDcp dist for HD 6409\n",
      "working on avg unit-to-HDcp dist for HD 7212\n",
      "working on avg unit-to-HDcp dist for HD 8016\n",
      "working on avg unit-to-HDcp dist for HD 8816\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set(list())\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u]% 1 > 0.23 and allUnits[u]%1 < 0.27:\n",
    "        barredJettisonSet.add(u)\n",
    "print(\"the barredJettisonSet (cannot be shed in below MUSTFREE code) is\",barredJettisonSet)\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 163 HDs\n",
      "working on HD 2178 cantFill 0 of 163 .Time is now 0\n",
      "working on HD 596 cantFill 20 of 163 .Time is now 4\n",
      "working on HD 1736 cantFill 40 of 163 .Time is now 16\n",
      "working on HD 3319 cantFill 60 of 163 .Time is now 18\n",
      "working on HD 5709 cantFill 80 of 163 .Time is now 22\n",
      "working on HD 6906 cantFill 100 of 163 .Time is now 28\n",
      "working on HD 8323 cantFill 120 of 163 .Time is now 37\n",
      "working on HD 277 cantFill 140 of 163 .Time is now 41\n",
      "working on HD 6021 cantFill 160 of 163 .Time is now 62\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.00296 0.13724\n",
      "CCB cluster 0 = unit 9020 with pop 179702.0 now has use of 1.0154212545161716\n",
      "CCB cluster 1 = unit 9021 with pop 27743.0 now has use of 0.855498943389568\n",
      "CCB cluster 2 = unit 9022 with pop 44076.0 now has use of 1.0185908456230246\n",
      "CCB cluster 3 = unit 9023 with pop 41430.0 now has use of 1.0699684707630983\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66e91b98-63a9-47db-af63-039ac47b1835",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "working on HD 5000\n",
      "working on HD 5500\n",
      "working on HD 6000\n",
      "working on HD 6500\n",
      "working on HD 7000\n",
      "working on HD 7500\n",
      "working on HD 8000\n",
      "working on HD 8500\n",
      "working on HD 9000\n",
      "out of 9111 total HDs, there were 8055 153 86 817 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 0\n",
      "working on current HD diam for HD number 1001\n",
      "working on current HD diam for HD number 2003\n",
      "working on current HD diam for HD number 3008\n",
      "working on current HD diam for HD number 4008\n",
      "working on current HD diam for HD number 5008\n",
      "working on current HD diam for HD number 6009\n",
      "working on current HD diam for HD number 7011\n",
      "working on current HD diam for HD number 8016\n",
      "working on current HD diam for HD number 9018\n",
      "here is the histogram of HD diameter = sqrt(area)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "print(\"here is the histogram of HD diameter = sqrt(area)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than 38018 but less than 190087\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 970 HDs to triage in this block, including some too discontig to fix here\n",
      "working on swell-filling discontig HD 57 our 1 th HD we can possibly swell out of 970 0.0 sec elapsed\n",
      "working on swell-filling discontig HD 356 our 11 th HD we can possibly swell out of 970 76.843 sec elapsed\n",
      "working on swell-filling discontig HD 1063 our 21 th HD we can possibly swell out of 970 84.692 sec elapsed\n",
      "working on swell-filling discontig HD 1924 our 31 th HD we can possibly swell out of 970 123.418 sec elapsed\n",
      "working on swell-filling discontig HD 2492 our 41 th HD we can possibly swell out of 970 147.874 sec elapsed\n",
      "working on swell-filling discontig HD 3929 our 51 th HD we can possibly swell out of 970 159.767 sec elapsed\n",
      "working on swell-filling discontig HD 4189 our 61 th HD we can possibly swell out of 970 179.891 sec elapsed\n",
      "working on swell-filling discontig HD 4846 our 71 th HD we can possibly swell out of 970 223.762 sec elapsed\n",
      "working on swell-filling discontig HD 5015 our 81 th HD we can possibly swell out of 970 241.243 sec elapsed\n",
      "working on swell-filling discontig HD 6064 our 91 th HD we can possibly swell out of 970 285.388 sec elapsed\n",
      "working on swell-filling discontig HD 6175 our 101 th HD we can possibly swell out of 970 320.41 sec elapsed\n",
      "working on swell-filling discontig HD 6491 our 111 th HD we can possibly swell out of 970 354.778 sec elapsed\n",
      "working on swell-filling discontig HD 6946 our 121 th HD we can possibly swell out of 970 416.255 sec elapsed\n",
      "working on swell-filling discontig HD 7574 our 131 th HD we can possibly swell out of 970 489.422 sec elapsed\n",
      "working on swell-filling discontig HD 8785 our 141 th HD we can possibly swell out of 970 496.952 sec elapsed\n",
      "working on swell-filling discontig HD 8960 our 151 th HD we can possibly swell out of 970 523.344 sec elapsed\n",
      "working on swell-filling discontig HD 77 our 161 th HD we can possibly swell out of 970 588.125 sec elapsed\n",
      "working on swell-filling discontig HD 190 our 171 th HD we can possibly swell out of 970 695.167 sec elapsed\n",
      "working on swell-filling discontig HD 349 our 181 th HD we can possibly swell out of 970 1137.572 sec elapsed\n",
      "working on swell-filling discontig HD 492 our 191 th HD we can possibly swell out of 970 1240.079 sec elapsed\n",
      "working on swell-filling discontig HD 567 our 201 th HD we can possibly swell out of 970 1277.92 sec elapsed\n",
      "working on swell-filling discontig HD 635 our 211 th HD we can possibly swell out of 970 1389.749 sec elapsed\n",
      "working on swell-filling discontig HD 733 our 221 th HD we can possibly swell out of 970 1499.085 sec elapsed\n",
      "working on swell-filling discontig HD 851 our 231 th HD we can possibly swell out of 970 1576.0 sec elapsed\n",
      "working on swell-filling discontig HD 1007 our 241 th HD we can possibly swell out of 970 1642.635 sec elapsed\n",
      "working on swell-filling discontig HD 1067 our 251 th HD we can possibly swell out of 970 1662.745 sec elapsed\n",
      "working on swell-filling discontig HD 1121 our 261 th HD we can possibly swell out of 970 1783.531 sec elapsed\n",
      "working on swell-filling discontig HD 1268 our 271 th HD we can possibly swell out of 970 1876.91 sec elapsed\n",
      "working on swell-filling discontig HD 1332 our 281 th HD we can possibly swell out of 970 1881.704 sec elapsed\n",
      "working on swell-filling discontig HD 1409 our 291 th HD we can possibly swell out of 970 1884.255 sec elapsed\n",
      "working on swell-filling discontig HD 1511 our 301 th HD we can possibly swell out of 970 1902.199 sec elapsed\n",
      "working on swell-filling discontig HD 1650 our 311 th HD we can possibly swell out of 970 2393.487 sec elapsed\n",
      "working on swell-filling discontig HD 1745 our 321 th HD we can possibly swell out of 970 2507.827 sec elapsed\n",
      "working on swell-filling discontig HD 1880 our 331 th HD we can possibly swell out of 970 2566.147 sec elapsed\n",
      "working on swell-filling discontig HD 2051 our 341 th HD we can possibly swell out of 970 2588.547 sec elapsed\n",
      "working on swell-filling discontig HD 2118 our 351 th HD we can possibly swell out of 970 2625.132 sec elapsed\n",
      "working on swell-filling discontig HD 2186 our 361 th HD we can possibly swell out of 970 2658.345 sec elapsed\n",
      "working on swell-filling discontig HD 2230 our 371 th HD we can possibly swell out of 970 2710.324 sec elapsed\n",
      "working on swell-filling discontig HD 2271 our 381 th HD we can possibly swell out of 970 2729.929 sec elapsed\n",
      "working on swell-filling discontig HD 2322 our 391 th HD we can possibly swell out of 970 2733.458 sec elapsed\n",
      "working on swell-filling discontig HD 2370 our 401 th HD we can possibly swell out of 970 2786.467 sec elapsed\n",
      "working on swell-filling discontig HD 2428 our 411 th HD we can possibly swell out of 970 2796.696 sec elapsed\n",
      "working on swell-filling discontig HD 2519 our 421 th HD we can possibly swell out of 970 2820.806 sec elapsed\n",
      "working on swell-filling discontig HD 2554 our 431 th HD we can possibly swell out of 970 2866.255 sec elapsed\n",
      "working on swell-filling discontig HD 2805 our 441 th HD we can possibly swell out of 970 2904.947 sec elapsed\n",
      "working on swell-filling discontig HD 2899 our 451 th HD we can possibly swell out of 970 2909.352 sec elapsed\n",
      "working on swell-filling discontig HD 3095 our 461 th HD we can possibly swell out of 970 2975.572 sec elapsed\n",
      "working on swell-filling discontig HD 3326 our 471 th HD we can possibly swell out of 970 2994.145 sec elapsed\n",
      "working on swell-filling discontig HD 3603 our 481 th HD we can possibly swell out of 970 3125.254 sec elapsed\n",
      "working on swell-filling discontig HD 3726 our 491 th HD we can possibly swell out of 970 3188.204 sec elapsed\n",
      "working on swell-filling discontig HD 3816 our 501 th HD we can possibly swell out of 970 3434.218 sec elapsed\n",
      "working on swell-filling discontig HD 3922 our 511 th HD we can possibly swell out of 970 3494.153 sec elapsed\n",
      "working on swell-filling discontig HD 4038 our 521 th HD we can possibly swell out of 970 3536.237 sec elapsed\n",
      "working on swell-filling discontig HD 4198 our 531 th HD we can possibly swell out of 970 3550.893 sec elapsed\n",
      "working on swell-filling discontig HD 4331 our 541 th HD we can possibly swell out of 970 3584.782 sec elapsed\n",
      "working on swell-filling discontig HD 4484 our 551 th HD we can possibly swell out of 970 3659.298 sec elapsed\n",
      "working on swell-filling discontig HD 4632 our 561 th HD we can possibly swell out of 970 3992.12 sec elapsed\n",
      "working on swell-filling discontig HD 4759 our 571 th HD we can possibly swell out of 970 4135.173 sec elapsed\n",
      "working on swell-filling discontig HD 4828 our 581 th HD we can possibly swell out of 970 4183.061 sec elapsed\n",
      "working on swell-filling discontig HD 4860 our 591 th HD we can possibly swell out of 970 4242.158 sec elapsed\n",
      "working on swell-filling discontig HD 4881 our 601 th HD we can possibly swell out of 970 4255.574 sec elapsed\n",
      "working on swell-filling discontig HD 5024 our 611 th HD we can possibly swell out of 970 4280.444 sec elapsed\n",
      "working on swell-filling discontig HD 5072 our 621 th HD we can possibly swell out of 970 4346.749 sec elapsed\n",
      "working on swell-filling discontig HD 5117 our 631 th HD we can possibly swell out of 970 4612.621 sec elapsed\n",
      "working on swell-filling discontig HD 5220 our 641 th HD we can possibly swell out of 970 4648.551 sec elapsed\n",
      "working on swell-filling discontig HD 5290 our 651 th HD we can possibly swell out of 970 4658.961 sec elapsed\n",
      "working on swell-filling discontig HD 5344 our 661 th HD we can possibly swell out of 970 4678.237 sec elapsed\n",
      "working on swell-filling discontig HD 5467 our 671 th HD we can possibly swell out of 970 4692.73 sec elapsed\n",
      "working on swell-filling discontig HD 5636 our 681 th HD we can possibly swell out of 970 4739.231 sec elapsed\n",
      "working on swell-filling discontig HD 5746 our 691 th HD we can possibly swell out of 970 4961.722 sec elapsed\n",
      "working on swell-filling discontig HD 5846 our 701 th HD we can possibly swell out of 970 5075.519 sec elapsed\n",
      "working on swell-filling discontig HD 5986 our 711 th HD we can possibly swell out of 970 5104.807 sec elapsed\n",
      "working on swell-filling discontig HD 6115 our 721 th HD we can possibly swell out of 970 5476.258 sec elapsed\n",
      "working on swell-filling discontig HD 6217 our 731 th HD we can possibly swell out of 970 5496.733 sec elapsed\n",
      "working on swell-filling discontig HD 6315 our 741 th HD we can possibly swell out of 970 5666.486 sec elapsed\n",
      "working on swell-filling discontig HD 6472 our 751 th HD we can possibly swell out of 970 5769.655 sec elapsed\n",
      "working on swell-filling discontig HD 6742 our 761 th HD we can possibly swell out of 970 5814.653 sec elapsed\n",
      "working on swell-filling discontig HD 6883 our 771 th HD we can possibly swell out of 970 5878.18 sec elapsed\n",
      "working on swell-filling discontig HD 6943 our 781 th HD we can possibly swell out of 970 6002.887 sec elapsed\n",
      "working on swell-filling discontig HD 7028 our 791 th HD we can possibly swell out of 970 6059.357 sec elapsed\n",
      "working on swell-filling discontig HD 7080 our 801 th HD we can possibly swell out of 970 6094.567 sec elapsed\n",
      "working on swell-filling discontig HD 7190 our 811 th HD we can possibly swell out of 970 6103.005 sec elapsed\n",
      "working on swell-filling discontig HD 7307 our 821 th HD we can possibly swell out of 970 6120.888 sec elapsed\n",
      "working on swell-filling discontig HD 7391 our 831 th HD we can possibly swell out of 970 6159.56 sec elapsed\n",
      "working on swell-filling discontig HD 7614 our 841 th HD we can possibly swell out of 970 6262.296 sec elapsed\n",
      "working on swell-filling discontig HD 7836 our 851 th HD we can possibly swell out of 970 6308.629 sec elapsed\n",
      "working on swell-filling discontig HD 7931 our 861 th HD we can possibly swell out of 970 6343.512 sec elapsed\n",
      "working on swell-filling discontig HD 8175 our 871 th HD we can possibly swell out of 970 6482.173 sec elapsed\n",
      "working on swell-filling discontig HD 8309 our 881 th HD we can possibly swell out of 970 6501.795 sec elapsed\n",
      "working on swell-filling discontig HD 8452 our 891 th HD we can possibly swell out of 970 6679.873 sec elapsed\n",
      "working on swell-filling discontig HD 8630 our 901 th HD we can possibly swell out of 970 6781.999 sec elapsed\n",
      "working on swell-filling discontig HD 8764 our 911 th HD we can possibly swell out of 970 6791.672 sec elapsed\n",
      "working on swell-filling discontig HD 8860 our 921 th HD we can possibly swell out of 970 6859.46 sec elapsed\n",
      "working on swell-filling discontig HD 8896 our 931 th HD we can possibly swell out of 970 6941.823 sec elapsed\n",
      "working on swell-filling discontig HD 8947 our 941 th HD we can possibly swell out of 970 6964.724 sec elapsed\n",
      "working on swell-filling discontig HD 8975 our 951 th HD we can possibly swell out of 970 7020.532 sec elapsed\n",
      "working on swell-filling discontig HD 9024 our 961 th HD we can possibly swell out of 970 7102.803 sec elapsed\n",
      "we partially regrew 893 HDs, leaving 163 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE  #Note -- this has not yet implemented the \"avoidedEnclave\" shortcut\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than\",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block, including some too discontig to fix here\")\n",
    "startTime = time.time()\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%10 == 0:\n",
    "        SEC = r3(time.time()-startTime)\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we can possibly swell out of\",len(triageList),SEC,\"sec elapsed\" )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.50 * aDP or contigPop > 2.*aDP - minPostFixPop:  #formerly < 0.75 aDP\n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 1.00296 0.13724\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00168 0.12532\n",
      "CCB cluster 0 = unit 9020 with pop 179702.0 now has use of 1.0035714553989967\n",
      "CCB cluster 1 = unit 9021 with pop 27743.0 now has use of 0.858626448639361\n",
      "CCB cluster 2 = unit 9022 with pop 44076.0 now has use of 0.9731986184609649\n",
      "CCB cluster 3 = unit 9023 with pop 41430.0 now has use of 1.0230387946367814\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1001 time is now 10\n",
      "working on HD 2003 time is now 18\n",
      "working on HD 3008 time is now 27\n",
      "working on HD 4008 time is now 34\n",
      "working on HD 5008 time is now 41\n",
      "working on HD 6009 time is now 49\n",
      "working on HD 7011 time is now 56\n",
      "working on HD 8016 time is now 61\n",
      "working on HD 9018 time is now 71\n",
      "out of 9111 total HDs, there were 9034.0 contiguous and 8876.0 complement-contiguous HDs\n",
      "49 HDs had both discontiguity problems, while enclave-only = 186 and discontig only= 28\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 77 186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 722332 district pop vs 760350 target\n",
      "we'll also trim any HD with total islands' pop <= 15207\n",
      "working on HD 57\n",
      "Out of 77 discontig HDs 77 had discontig HDs.\n",
      "Of these, 1 won't be under 722332 after all minor discontigys were shed, while 76 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 76 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "153 817 76\n",
      "{2050, 2051, 6146, 8196, 4105, 6156, 6157, 6158, 2063, 2064, 2067, 6166, 2073, 8218, 2076, 6173, 6174, 6175, 8961, 8226, 2083, 8227, 4133, 4137, 6186, 4141, 2098, 2099, 52, 4148, 2102, 2103, 64, 8968, 67, 68, 69, 2118, 72, 6217, 2122, 75, 2123, 6218, 77, 4175, 78, 2126, 8274, 84, 6222, 4178, 85, 4181, 8971, 6234, 4189, 95, 2147, 2149, 4198, 6249, 107, 2158, 4206, 8302, 6942, 2163, 8309, 6943, 8312, 8314, 124, 8316, 6270, 6271, 2175, 6275, 2184, 2185, 2186, 2187, 2188, 4232, 6284, 2192, 2193, 2194, 2195, 2196, 2197, 4247, 4248, 2206, 2207, 160, 4257, 162, 163, 6305, 6306, 164, 6307, 8358, 169, 6314, 6312, 6315, 4268, 6316, 8373, 2230, 2232, 6329, 2234, 6331, 6330, 8376, 193, 2243, 197, 4293, 202, 8395, 2262, 2263, 2264, 2265, 2266, 4312, 220, 221, 2270, 2271, 4323, 4324, 229, 6374, 6372, 8420, 2279, 2280, 2282, 4331, 2284, 6383, 2289, 242, 2290, 246, 8446, 8452, 261, 8455, 2314, 2315, 4368, 2322, 276, 6422, 2328, 6423, 6424, 283, 2333, 6430, 8477, 2336, 4384, 4386, 293, 311, 6458, 2366, 2368, 2370, 2371, 6472, 2382, 6478, 6479, 2385, 2386, 2387, 2388, 8535, 4442, 6491, 8538, 6492, 349, 6496, 6497, 355, 356, 357, 2404, 6501, 6502, 4457, 4463, 4464, 4465, 4466, 2419, 4467, 8565, 8571, 2428, 2429, 8579, 4484, 4487, 8585, 396, 398, 399, 402, 8598, 4515, 2470, 2474, 4524, 2480, 2481, 2482, 2483, 2485, 8630, 8632, 441, 443, 2492, 2493, 454, 4555, 2508, 4556, 2510, 463, 8659, 2519, 2520, 2521, 2525, 8671, 2530, 489, 2538, 492, 494, 2542, 2543, 4591, 4594, 4600, 2553, 2554, 4602, 4608, 522, 6666, 2578, 531, 8722, 8724, 8725, 4632, 537, 8733, 544, 8737, 4641, 546, 2597, 556, 8753, 563, 8758, 567, 4664, 8761, 6715, 571, 8764, 8770, 8772, 582, 4680, 2636, 589, 4684, 4688, 8785, 8786, 4690, 6739, 8789, 6742, 6750, 8801, 8805, 4711, 4713, 8810, 619, 620, 4717, 4719, 623, 4724, 2679, 632, 633, 8267, 634, 635, 636, 637, 8831, 4727, 4731, 8836, 646, 2696, 6793, 6794, 2699, 654, 6798, 6801, 659, 4756, 8853, 4759, 8856, 4760, 8858, 8857, 4763, 8859, 8860, 8867, 4773, 8870, 8872, 681, 8874, 8876, 8877, 686, 8884, 694, 8886, 6841, 8890, 4798, 4800, 6848, 8896, 8897, 4807, 4808, 2763, 8910, 2768, 6865, 4818, 4821, 8918, 727, 729, 6874, 730, 4828, 733, 4829, 4830, 4831, 6881, 6882, 8929, 4832, 738, 4833, 6883, 6885, 6890, 6886, 744, 4846, 6887, 6888, 2797, 8941, 8946, 8947, 4852, 2805, 8948, 6906, 4857, 4858, 765, 8957, 4863, 767, 8960, 8958, 8959, 764, 2814, 4862, 4864, 771, 4869, 4876, 775, 4871, 2828, 4875, 4877, 4878, 4881, 8974, 790, 8975, 6936, 8979, 791, 8987, 4892, 4893, 2842, 4895, 4891, 2844, 6946, 4894, 4896, 4897, 6944, 6947, 6948, 8999, 6954, 9000, 9007, 9013, 9015, 9020, 9023, 9022, 9024, 2884, 2885, 9033, 6986, 845, 850, 851, 2899, 854, 4953, 9057, 9058, 9060, 7015, 7016, 7018, 4971, 9068, 2926, 9070, 2930, 4979, 7027, 7028, 2936, 889, 7031, 891, 4988, 2939, 7036, 7037, 7038, 7039, 7040, 2946, 7041, 9091, 9094, 7055, 2961, 9105, 2963, 5015, 919, 925, 5024, 5025, 7079, 7080, 940, 941, 5037, 5038, 7084, 7085, 7086, 7087, 7092, 5044, 5046, 5047, 5050, 3016, 973, 5072, 3026, 979, 3033, 5087, 5089, 5090, 5091, 996, 5094, 5095, 1007, 1008, 5114, 5115, 5116, 5117, 5118, 7162, 7163, 5123, 7179, 5132, 7185, 5138, 7190, 1047, 3095, 1049, 1055, 1056, 5153, 7202, 5155, 1060, 1061, 3108, 1063, 5160, 5156, 7207, 1065, 1066, 1067, 1071, 1072, 1079, 3130, 1086, 1088, 1089, 7233, 1091, 1092, 1093, 7238, 1096, 7243, 1100, 3149, 3150, 7247, 5205, 1110, 1109, 1121, 1122, 5219, 5220, 1129, 5225, 3179, 5227, 7274, 7281, 7288, 3193, 7303, 5259, 7307, 7309, 5263, 5266, 5267, 7315, 1173, 5270, 5272, 7320, 7325, 7327, 7328, 7329, 5289, 5290, 1195, 5291, 5293, 7343, 1204, 1207, 5303, 5322, 7374, 5333, 5334, 5336, 5337, 1242, 1243, 3292, 5344, 7391, 1260, 5361, 7411, 1268, 1273, 5369, 1276, 3326, 1281, 1283, 1285, 1289, 1291, 5390, 7455, 1312, 7456, 5410, 5411, 1331, 1332, 5429, 1334, 7479, 1335, 3382, 3383, 3384, 5433, 5434, 7487, 1347, 1348, 1349, 5443, 5467, 3421, 1384, 5480, 5482, 7538, 5492, 5493, 7543, 1402, 1403, 1406, 1409, 7554, 1410, 1411, 1426, 7574, 7584, 7586, 7587, 5541, 7614, 7615, 7616, 7620, 1483, 5581, 3534, 5583, 5582, 5584, 1492, 1494, 1495, 1496, 1497, 3546, 1511, 1516, 1527, 1528, 3581, 3583, 3586, 5635, 5636, 5638, 1545, 5645, 3612, 1566, 1567, 1572, 5685, 5686, 3641, 7739, 3643, 5701, 3659, 7756, 3676, 1632, 1633, 5732, 5734, 5737, 3691, 5743, 5744, 7791, 1650, 1651, 5746, 5747, 3702, 5749, 1656, 5754, 5755, 1661, 5763, 7815, 3720, 1673, 5771, 7819, 3726, 7823, 5781, 7830, 3734, 3735, 7836, 7838, 3746, 3747, 7843, 3752, 3758, 3760, 1718, 5814, 1721, 1725, 3774, 1729, 3779, 7876, 7878, 1740, 5837, 7887, 1745, 7890, 1747, 1748, 5845, 5846, 1752, 5849, 7899, 1756, 7901, 1758, 1759, 1760, 7902, 3814, 3816, 3818, 5866, 5868, 5876, 1781, 7927, 7931, 763, 3839, 7939, 3845, 1798, 3856, 3857, 7964, 1822, 1823, 7967, 5928, 5932, 6878, 5934, 5935, 3890, 5944, 3906, 8006, 8007, 1868, 3919, 3922, 3926, 8022, 1880, 1881, 5981, 5985, 5986, 5987, 1891, 1893, 3942, 8925, 3947, 8045, 3950, 8926, 1912, 1913, 4859, 6017, 3971, 1924, 4860, 6020, 4861, 8077, 3982, 8934, 1942, 1944, 6041, 8935, 1948, 6051, 6058, 4011, 1964, 6060, 1967, 6064, 6065, 6061, 6068, 1973, 6069, 1975, 6074, 1979, 8124, 4038, 6089, 6910, 4046, 6911, 6913, 8161, 6115, 4071, 8175, 8176, 8178, 4086, 6134, 8187, 6142}\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 76\n",
      "swell-generating HD 57 our 1 th HD we must regrow out of 76 0 sec elapsed\n",
      "swell-generating HD 3225 our 21 th HD we must regrow out of 76 216 sec elapsed\n",
      "swell-generating HD 4794 our 41 th HD we must regrow out of 76 493 sec elapsed\n",
      "swell-generating HD 6889 our 61 th HD we must regrow out of 76 675 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for iii,t in enumerate(badDiscoList): \n",
    "    if iii%20 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",iii+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDunitList[t], unitNbrs) in above code block\n",
    "    altContigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    altContigPop = np.sum( [ unitPop[u] for u in altContigUs ] )\n",
    "    if abs( altContigPop/aDP - 1.0) < 0.4 : #MO and later -- MAKING THIS MORE LENIENT; only regrow from scratch if contig pop > 40% off\n",
    "        contigUs = altContigUs.copy()\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "318098a2-b495-4279-991e-3981f49479b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for squaring up\n",
      "Must have already established unitGeoms, pops, topology in above blocks\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./2024state_HD_output/IL10084opt3ContigButOffPopUnit.csv ./2024state_HD_output/IL10084opt3mostlyContigButOffPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of HDweight should be unity, actually is 0.9999999999998778\n",
      "I will now renormalize\n",
      "normalized weight is now 1.0\n",
      "I read in 10084 HD lists of units\n",
      "weighted unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1600\n",
      "working on avg unit-to-HDcp dist for HD 2400\n",
      "working on avg unit-to-HDcp dist for HD 3200\n",
      "working on avg unit-to-HDcp dist for HD 4000\n",
      "working on avg unit-to-HDcp dist for HD 4800\n",
      "working on avg unit-to-HDcp dist for HD 5602\n",
      "working on avg unit-to-HDcp dist for HD 6402\n",
      "working on avg unit-to-HDcp dist for HD 7202\n",
      "working on avg unit-to-HDcp dist for HD 8002\n",
      "working on avg unit-to-HDcp dist for HD 8802\n",
      "working on avg unit-to-HDcp dist for HD 9602\n"
     ]
    }
   ],
   "source": [
    "#restart pick up from here next day - triage, swell remaining ....\n",
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for squaring up\") \n",
    "print(\"Must have already established unitGeoms, pops, topology in above blocks\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigButOffPopUnit.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse,weights = unitPop)\n",
    "print(\"weighted unit use histogram\")\n",
    "plt.show()\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "c1b66c69-a0d5-4222-be34-fe0bd21ab6e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 501 time is now 56 sec\n",
      "working on HD 1001 time is now 101 sec\n",
      "working on HD 1503 time is now 125 sec\n",
      "working on HD 2003 time is now 152 sec\n",
      "working on HD 2508 time is now 220 sec\n",
      "working on HD 3008 time is now 236 sec\n",
      "working on HD 3508 time is now 256 sec\n",
      "working on HD 4008 time is now 271 sec\n",
      "working on HD 4508 time is now 286 sec\n",
      "working on HD 5008 time is now 315 sec\n",
      "working on HD 5509 time is now 368 sec\n",
      "working on HD 6009 time is now 414 sec\n",
      "working on HD 6510 time is now 442 sec\n",
      "working on HD 7011 time is now 453 sec\n",
      "working on HD 7513 time is now 472 sec\n",
      "working on HD 8016 time is now 498 sec\n",
      "working on HD 8516 time is now 513 sec\n",
      "working on HD 9018 time is now 692 sec\n",
      "out of 9111 total HDs, there were 9031 0 80 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 723 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "70a788e5-8a54-499a-bbb9-793d39a3e7c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 798367 district pop vs 760350 target\n",
      "working on enclave-y HD 67 . We have evaluated 0 of 80 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 6060 . We have evaluated 50 of 80 enclavy HDs.Time is now 48\n",
      "Out of 80 HDs identified as enclave-only 80 had enclaves.\n",
      "Of these, 76 wouldn't be over 798367 if all enclaves filled, while 4 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(enclaveOnly):\n",
    "    if iii%50 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(enclaveOnly),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(enclaveOnly),\"HDs identified as enclave-only\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "653826fd-0c3b-433e-8fd2-717100d961a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "We still have 8 HDs that are contig but with an enclave\n",
      "There are now 0 noncontiguous HDs\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in enclaveOnly + discontigOnly + bothProblems :    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"We still have\",len(stillHasEnclave),\"HDs that are contig but with an enclave\")\n",
    "print(\"There are now\",len(badDiscoList),\"noncontiguous HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "cf7846a0-b96a-4b41-b1e3-92b98a96704e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Hpop = [HDvPop[t]/aDP for t in popHDlist]\n",
    "plt.hist(Hpop,bins=[0.5,0.6,0.7,0.8,0.85,0.9,0.95,0.97,1.,1.03,1.05, 1.1, 1.2, 1.4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "f463f97e-ed0d-46be-9da7-4fa5d4883b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filling enclaves for HD 2118 ; pop/aDP will now be 611926.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filling enclaves for HD 3179 ; pop/aDP will now be 848083.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filling enclaves for HD 5156 ; pop/aDP will now be 821482.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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3smPhfOa/+jy5aSmODsvuXNx8cfGTOb37JFmpBx0djiAIgEhYhMZKUtWGP2G/oKAAT09P8vPz8fDwcHQ4FaScPMby/31IcV4uTbt0R+/igt7gTOaZBIKbt6LfbXc6OsRaObrrN5Z/8B06Fyt3f/g17t5VXyBSEAT7s1pNbNjYhjZtPiA46EZHhyMIV1Sdz2/Rw1LHQlpGc+e7n9Jh6AhKCvLJiD/N6f17KMnPY+efC0g/HevoEGulTY+bmfjq05hLNOxc9rmjwxEE4RwxhkVobEQdlmtAb3Bm4JT7K2xTrFZ+eOEJNv08j5tfmemgyOzDxcMPAMVqcXAkgiCcJ2YJCY2N6GFxEFmjoXXf60g/3fDX4Vk663WcPKz0uP5hR4ciCP965UsJNfyr/YJQgUhYHMjVyxtTcTGWsjJHh1IrpXnFWIyg1esdHYogCOWXgkTCIjQuImFxIA//AAAKszMdHEnNZSQfoDRHC5KExL+nyq8gCIJwbYmExYHc/Wzr7xRkNcyEJSl2E7+8Mh29m5U7330fVw+xnpAgOJ7oYREaJzHo1oFk2ZYvqg20sNzKLz5E1kjcPvMjfINaOTocQRCA8wmLGHQrNDaih8WBclLOAqBzdnFwJDWTn2xBVVV8g0WyIgj1hXR+1K3IV4RGRiQsDpSRcBqAHQt/IeXkMQdHUzNWk6j1IAj1i7gkJDROImFxoI5DR9H3ljsoKcjnt5mvkJ+R5uiQqiWorTtaA1itDXuWkyAIglD/iYTFgQxubvSaeBu3zXgHrU7PwdXLHR1SlR3d+Sv5qfmUFcnsWz3X0eEIglBO9LAIjZNIWOoBncFARIfOnNy5lYawtJO5rJhV//uO0hwZzyYSPsGRjg5JEIRzzo9hEYNuhcZGzBKqJ1r3vY4T2zZRlJONu6+fo8O5LFVV+fOTh7BaJPrfM5AeI551dEiCIFSmAXz5EYTqED0s9YRPSCgAWWcSHBvIVcQeXMyZPbm0Hd5eJCuCUK+JhEVoXETCUk84e3gCELdvt4MjubKtv/6AxslK7xvEukGCUH+J2XtC4yMuCdUDZaUlzHvalgB4nivXX1/lJJSiKhq+eeRRAJzcVaa8/Q0efsEOjkwQhAskMYZFaHRED0s9cPbEMUry87jznU/pfsNER4dzRe1HdcYn0oDO1fZmaCqUSD+7y8FRCYJwsfLicYLQiIgelnog9dQJABZ/+F/C23Vk+AOPOziiyxs2ZWb5/W+eGUd+spXwlgMdF5AgCJUTg26FRkb0sNQDrXr3p9eEW5FlDVmJCY4Op8pcvJwBSE3Y6eBIBEGoSPSwCI2PSFjqAd8mYfS99U4Mrm64eHlRkp/n6JCuqqQok9TDRXhHaAlvNdTR4QiCUIGEmCUkNDYiYalHtHo9cXt2MvuRqRTn5To6nCs6sO4nAAKaRZSvOi0IQn0hBt0KjY/4pKlHbnjmZW7+v/9itVpJOLjP0eFcUds+NyDJKifWxfHZvdfz69t3cXTHLyhWq6NDEwQBED0sQmMjEpZ6xODmRni7DgRGNSf+wF5Hh3NFnn5NuefT/9FqcATBbf1IP5nB8o9+YvGsRxwdmiD860kSYtCt0OjUKmF5++23kSSJadOmlW+bPXs2AwcOxMPDA0mSyMvLu2o7M2bMQJKkCj+tW7euTWgNWlSnLiTGHEBR6ndvhZd/FGMe/Jybnv6eR2YvoklnTxJ2n8FiLnV0aILwLycG3QqNT40Tlt27d/PVV1/RoUOHCttLSkoYOXIkL730UrXaa9u2LampqeU/W7ZsqWloDV5kx64YCwtIPx3r6FCqTKs1ENA0CqtJQ1bqIUeHIwj/cmLQrdD41KgOS1FREZMnT+brr79m5syZFZ4739uyYcOG6gWi1RIUFFSTcBqd4BatcHJxJeHAPoKbt3J0OFWm1dqmOZuNJgdHIgj/dmLQrdD41KiH5dFHH2X06NEMHWq/6aynTp0iJCSEpk2bMnnyZM6cOXPZfU0mEwUFBRV+GhNZoyGifSfiD+xxdCjV0nnIvWj0CruXfu/oUARBEIRGptoJy/z589m3bx9vvfWW3YLo2bMn8+bNY8WKFXzxxRfEx8fTv39/CgsLK93/rbfewtPTs/wnLCzMbrHUF1FdupMae5Ki3BxHh1Jlbp5B+Df3ID0uxdGhCMK/miRJYtCt0OhUK2FJSkriySef5KeffsJgMNgtiFGjRnHzzTfToUMHRowYwbJly8jLy2PBggWV7j99+nTy8/PLf5KSkuwWS33RrFtPZFkmdtd2R4dSLQFRkZRkyhTkXr6HTBCEuiYG3QqNT7USlr1795KRkUGXLl3QarVotVo2btzIp59+ilarxWqnGhxeXl60bNmS2NjKB506OTnh4eFR4aexcXZzJ7xdR07u3OroUKrFwz8QgO1/fe7gSATh30wMuhUan2oNuh0yZAgxMTEVtt199920bt2aF154AY1GY5egioqKiIuL484777RLew1Vi559WfP152z//RewmlCT96FazaiqiqooFBaZkACDQQOqatuuquX3/7nNZDQR0qwpehc3ANt2Lt4f4Nx9wODhRatbnkWqRiXbnqOeYsevazm69hCBUd/Sod8UZI1YY1MQrjUx6FZobKr1SeLu7k67du0qbHN1dcXX17d8e1paGmlpaeW9IzExMbi7uxMeHo6Pjw9gS3zGjx/PY489BsCzzz7L2LFjiYiIICUlhddeew2NRsOkSZNq/QIbshY9erP370UcWLUUijKQJRUJFUnWIEmQb9IB4ONsRpJs36nOLyt/fnV5W10bsFpVMou1nDx7/DJnU8s7kW3zCyRUJEJ6jsQjqsNljqncLTNe5s/332DtrD/Iy0hi4M2vVf/FC4JQSyJhERoXu3/1/fLLL3n99dfLHw8YMACAuXPnMnXqVADi4uLIysoq3yc5OZlJkyaRnZ2Nv78//fr1Y8eOHfj7+9s7vAbF2d2Duz/60jZ4bu71cGab7Ym7l0NEn2q3p1osqChIyCDLV+w5ObP6e377ZgFKWfWnKAdH9mbYfU/z11uflE91FgTh2hGDboXGqNYJyz/rrcyYMYMZM2Zc8ZiEhIQKj+fPn1/bMBo3SYJ7lsOZHfDtCJg7Cl7NAbl6l+AkrbYGQ/GUah8BcDbONljYJySqRscLglAbYtCt0PiItYQakvBeMOhl2321ZolElUk1e8NTFAuxB5dwcOlW/FoYaNPzZjsHJgjC1alYrMWODkIQ7EqMhmxofJrabn+5De5YWGenkc7lsqpS9W7ltIR9LP74VQpTQe+uod+t99VVeIIgXIHVaqKk5LSjwxAEuxIJS0PTapTtNnYN5CaAd2TdnEc+38NS9YRl04KPKUyFjmPbM/C2GWi1TnUTmyAIV+Tu1hqNbL9aWYJQH4hLQg2N3hUmzrHd/6Qj5MTXyWmkc9fAq9PDMuSu/0PSKhTlZolkRRAcSJJ1jg5BEOxOJCwNUdsJcP37tvt/PFA355CrN4bFXFbMid1/oFpkZK14sxQEQRDsS1wSaohkGXrcD8cWg2SfYn2VnOTcbdV6WDb+9iYHFx/Cs4mWPjc+UkcxCYIgCP9WImFpyLzCbVOdrRaoo2qye3/5HCeXuSiKgmpVUM5V2VUU1bZNtd0/nlCMh3MpE9vmIC2bTN656rqSqpRX0L1AvejhuTsWE6qzF843/oCTb8XihIIgCIIgEpaGrN1NcHA+/HGfbVxLNeuyXIlnZBt8nC0knMlBlrKRJNtVIlmyFaWy3YIsScgyhHkb6R52Fm2xFSRQpXM7SNK5GrrnRsVIF9fUvTBWxjUnF42Sg/pZX3K734DX9d+XV+0VBEEQBJGwNGTNBsHN8+DXO6DV9dDhFrs17R7ZjrvnrbBbe1VhTN+NceU0vHcvJle+C+9R31/T8/9bJece5pHVU8kpM9PLL4qne39AiGczR4clCIJQgRh029BFj7XVZtkxC/KSHB1NrRgCu+M0aKbtQTVmJwk1V2rK445lt5NuMuGmldmaGcfNS8azI/FPR4cmCIJQgUhYGoO+0yBlP8y73tGR1Jpx10cAuA18x8GR/DvsS91ItkXlje6PsuK2/Sy8YQFlisKLm18FwGoqwpiViKLUcWVlQRCEqxAJS2PQ9S4Y9h/IOwOmQkdHUzvBtpWhraUZDg7k3yHSOxqA3SlbAAjxjOZWlza8976ZY62jOdmxO/H9RnLq/25zZJhCtdnWXBeExkSMYWksmnSz3X53A4z/CvxbOjaeGtL5tAbAkh8Pfp0cG0wjk1YQy6xdL2O0lnFd2GBKLKW09rUliAXmovL9burwPKXGO8sfqzoV88ajFJ7Yh3urLtc8bkEQBBAJS+MR3htGvQfLn4O/HoX7Vjs6ohrR+bVHkUBZNR3lnkHITl6ODqlR2Hx6Ps9sfRNVBVlSWZ4WW/6ck6TyYr/PAVj23CTC/z6ABjjbzJPw12fiVZRJxkv/4cytk5Gvj6bVzN+RZdE5KwjCtSUSlsZCkqDLnbD7a6qz/k994+TXkdxeN+G9/XcKvu2Fx8PHHR1So7AibiFlCqyasBhnnQfJ+cdJKYhjzqEveK7nq/i4hrJ//a9E/H2AhGHRRN//DEPb9y0/3vhcIsXTv4M/jlF830Hcm3Z24KsRBOHfSCQsjcnC+2wLIk76xdGR1Ir3iDnkmEvx3rMUU34cTmKKba2FuYdj5TjeLqHoNHqiDX2JDuzLkBZTAEiNPYj16ddJC3Nl+Ls/oTM4lx+bOO8VSt5eiDXSgP+z00SyIgiCQ4h+3caiKBNOroTu90PzoY6Optb0TUciAaUHv3R0KI3CyqTNuMlQWe+bqbSI4w/eTamTTPsffquQrABQZjsm6vsFBA69q+6DFQRBqIRIWBqLM9tBMdsuCzUC+ib9bB+tmScdHUqDZTIX879tT9D1+/bElpRyf+ux6DQVV9He89fX7B3YC5+0Urzef5OAwKgKzxclHaZw3ToAzOnJ1yx2QRCEfxKXhBoDxQp7vgXPcPBv7eho7KJw3VP4AhbPAFRVFWX6qyGzMIEf9v+HHxN2YVahu5cfWklmRMt7Kux39uR+XF/4EFeg6J2n6d5v3CVtJb3/ApoDeWgmd8Wj7XXX6BUIgiBcSiQsDV3OafhlEmSegMm/2QbfNgJuvV6h9PQ+/LYsIKe0EJ+x8x0dUr23N3k5z216mSxzWfl6TWND2vLfYZf+7owlhRx96iF8nSS8v/uS6E4DKm1TSc9HbeNG9P/9WKexC4IgXI1IWBq6nbMh8zjc+hO0GOboaOzGKag7PBmP5a1AdAm7HR1OvZdZdIYnNjyPs6zhqTajGdHyPjydA3HVe1TYT1VVDi39nsI33yco30L+fx6hy2WSFQBVskCJua7DF+pEw50tKAiVEQlLQ5d1boxH9BjHxlEXNFoKW/fG5eQ2R0dS7y0++hmFVpUfR84hyq9rpfvknz7J7tceJ3T3GYqjXAma9SntOw+6csNWFcnXtQ4iFuqSROPoaRWEi4mEpaHTnhtEWZwNrr6OjaUuFGciqeKb4uWoqkp85ha2nd2CiyxfNllRFIVjt4wntEgh8c6BDHnhE3Ra/VXbl/09UbcnYS7KQefmY+/wBUEQqkwkLA3d8JlwYhkk74JWoxwdjV3lbngS7/jjZPeeQCNMxWplT9Iy/rtjBmdKS87POuaGkDaX3T/p1F48ixTiHh7JmCc/qtI5LKXFKDGpyEYVU2aiSFgEQXAokbA0dD5NwSsctn5quw1s6+iI7EYTu8l26x6GqliQZPHnCvDmhqn8mriHQJ2GWyN7E+LRlOHN78DfLeyyx5w9sA1voNvYe6t8nrLCNDQZFqQBkbhFiWJxgiA4lqjD0tBJEoz+CHLi4Iu+sO97R0dkN05jZlPo64PXqk/IWzjW0eHUC0WmbBYk7qGvtz+Lxq/kuQFfM7nT9CsmKwAlJ49TbICAyMv3wvyT3jsIq7+MtSC/tmELgiDUmkhYGoMWQ+GpI9D+Zlj5iqOjsRunoO64Px5PkYcLmvR/ZwG5hJyD5JakAbD37FpG/jYQFXi428u4OQdVuR2T1YTOApirPuNn/5t90GQqeN9+RzWjFgRBsD/Rx95YaHTQaiTELIAdX0Kvhxwdkd2Ym/bB+8Aa8g/PxrPdA44Ox+5ikpex4Ph36GU9VlWle3APvJ1DeXf328SVGtEA48M6sShpPxLwn24P0iGkessvuAQ1QW8Bo2LCBaerHwA4GyKBkxhTYlEURazQLAiCQ4mEpTFpMx567YEVL4BPFLQc4eiI7MJr7HxKT4Vh+OsFChQFjw6NIxkzmYt4fvXNbMhMQkHCQwMWFRYmHwTAXQPT209kd+o2fk86gK9WZu6IuZedCXQlJanJGPXgbHCv8jHRT//KiRMjMX28gmMbd9L8f7/j5BtS7XMLgiDYg0hYGhNZhhH/hfTDsOAuGP0BdJ7s6KhqTdLo0Ny7Hsucgcjr34IGmrCk58XgagjGRe/Dj/tnsCR+JSeKi7m3xTAe7PEmBq0LVsXKobQtnM07RKfggTTxbs/tQHFZMQatAY2sqfZ5s9MT8ft7B6ltAulcjUrIGr2BNnM3kPT7OxS9Mo/Er1+i5YvzKuyjqiqgoqoKtkJlClarGVU1oSgmrFbbraKYUNQyFKsJVbXYjgFQFZBs7dgiO9+WBCjnSp+p508GqEiSDlnWo9G4IMt6ZNkJWXZCkjTnlnCQsDUhXVSPREKSJSRJLn8M0rl9Ljw+f3zFxxftDxftY4vNqlixqhYkLpxPluQKRacLsktxctEhSedfkXrRy1LL2+LcfVW9OP6L4jl3q3LheVVRzv3eLrRttViwmBWK802X/sNeVCXg/KklGVw89GIJDKFeEwlLYyNJcNsvsOw5+OtRCO4AQe0dHVWt6X2iKe5xB/FLsnBdt4hWg8c7OqRqeWr5WNZmxOOhkWjvGcSWnDRC9Bre7PYAY9s+Ub6fRtbQOeQ6OodUXLfHVV/z4m3b3niCEItK13dqtvK1a/OOFAEF8dvYNLc55hAVdDUOR7hGkmJc2fLF1irvP3hKa6L7iB40of4SCUtj5OQGN3xqW8F58wdw8zxHR2QXmoiH2F6UBAsgIXkBI6bc4uiQqkRVVQ7kJKIi0dojgN25qfT0CmD22DV1Pi4kvyAD/x0nyR3Wla4RNVsY07VJe6xeKq4bNbhu1FAWpmCeGom+VSSyszMSMhd6LGRkWWvrBZH0SLIWWdYhoUOWbT9IGtsxF/VelPckXNwrUv68fFEvCKiq5VzvjRFFLUNVylBUM6piQVUtJJ/9AYCm/B96Z+9zbagXeoOw3XJRj8S5hm3buXi/f2xXzz+jIqkSBTGpaC0X9XqpEts8DhLYLQpFvdA/ZDylpeyEc/lrkLzNNI8OwcXF+QpVaVWQLo4JLo7vfOTnf3flPT/nbsO7dqRrD/8LzVVymvM9Kiu+PkxpoViCQajfRMLSWGl00OsRWP6cbYHEbvdA5ym2y0YNTMHpWHbMXcepzOaAgrNHPrHb/BhwUx5arQu7V60mJy2LrkN7ERzZytHhXkKSJG6N7MXnsduZ3uddmvp2QpKqf2mnJmLWLMC3GEJvvb/GbTj5hdJq3XaKju+mND2W3Jc+Q/+fM1gj0mn1x3p0rt5Xb+QayorbhKwaiBo9tc7Ptc+wlZyNSUh6DSVyKe1Tomif35Se906ouGMn283emKNsWHwI/WFfdh6xoEbnct+9N+Du4lbnsV6JRiMuBQn1n0hYGrMe94NHMBycD0uehDM7YHzNLgs40pktezmV2ZzW4Sl0Gd+FTJMbq78sYd5zO9C7F2LM80bW+XFmXwJBrXcxYupYXD28HB12BT5uUcB2isoKrlmyAlAUcxBnJ2jRqXet2tG5eOPdZTjeDMen+xhSF3yC8dNlxL54O9GfLbdTtLVnMRZhlvOR1dJrcr4uPftCT9t9s8XM4f+swNmip8xsQq+7dDZW1/Zt6Nq+DYlpZ/l7yRbM+32Y9Z+l3PPscPx9HZf4SZJ00VgaQaifGt7XbaHqJAmix8JtP0FAWzj4C8wdDevfAmvD6f71jbB1a0d1CcE7ug0tO/Vi2EMuKFY9pgIPBt1n4O53BhDeJZO0k74s+WIZqQkniYvZR5nR6ODobVp42wq2rTv92zU7Z0luJoZN+zjT3AOt7urrBlWVwS+cgHG2Hhunli3t1q49pB5bhlWfRxPXu675uXVaHca+Bnwsnmycs+iK+0YEhfLo/bfS9QFfNEXOzP7fYscmDKKDRWgARMLybzF0BrSdAM5esPFt+LI/WCqZQVAP6Ty9AHC66Atry069uGNmR6a81Zk23fphcHFnzH13ED2wiOz4IP54O5kVn+cx7+W/yUpNdEzg5xSWpvDclldxkSVGtbizzs9nLi1hxYz7ODroOrzTS2hy94N2P4clPxMAQ7P6lbC4uEUA4OQZ4JDzF2bkAqAJNlRp//6duhM91huv1DB+Wvx3XYZ2RZJ0YcaQINRXImH5t2g5HG6ea+tt6f8MZB6D3ARHR1UlWv25K5eair0Enn6+uP3jg6nfDTfQ93Yr3W4sZvB9gCqx9IutWB2YnFksRWSarYxp0pnWgbW7NFMVKx+8gbD5W0nt3wrPxT/Tfcw9dj+Heyvb6yjcttHubdeGs5dtlsvJtBkUnj19zc/feVh/ANwOKpjNZVU65vph/Sj2z+TMXgcvgSASFqGeEwnLv9GA58A9GLZ+4uhIqkTn4QGAMb/o6vvqDXQaMIyeI8cS3W0w/ScFUZQRxJePbSXxxJG6DrVyWh8kJPKM2XV+qty0REL3n+Xs+J6M/WwRkU3rZtFCa2EBALrwJnXSfk25+IfRrcVfSFYdR3Y/jaIo1/T8+1bZFuwMKfEnLTmpSsdIkoTO34qmuGq9MnVCkhAZi1DfiYTl30hrsM0iyjvj6EiqJP1wPAAGr6pXaT2vVdfetB+VB8COxQdQrFZ7hnZVVsXMo8vHoZVUbmx5W52fb88XM5FV6PD4y3V6nsxtvwLg3KLqiynWtYyja9m9fBLHDk7Hasij2COG3Ljd1+z8B3Zup+2REPJ0RaRPlAmLalblY1UrqJpr+7d5MQlxSUio/0TC8m+kqrYeloTNsOtrR0dzVTEbzhDkkkhIn17VPlaWZQaMm0D3iYVkxQWz8Y+/6iDCyzuVuYuYwgJ6+UXRv9k1WEQwI5syrURQSIs6PU3BrvUABPafWqfnqarTm78kJu0ByiwZ6OUAvEx9cS/tiqtf5DU5v6nMiN8iC1asuN/Tgq7d+1breGuBDPpr2xtUgRh0KzQAYlrzv5Esw9SlsOhB2PiubfpzPZN1YD/ZCVk06RbN2bwQerWNR9LUfDpwj2HjOLppPqmnru1YlgifjkioZJXmXJPzmWSFIo+6+W+tqipZG+aT+ePXSFtTUXsGImsd9xaStPdXtHiQX7iXs9Z5uBk70G3UAjTaa1+Gd+dnS2hKEKljVHpFNa/28ZIWLEZLHURWxfMDqiK6WIT6TSQsjZBaVkb+kiUUrluP++BB5C5YgD40FK9bbsW1l61ohGpVyTsuox4rwsdSBlr7TXu1h6OLtxCT0hZWnEQCWo3oUes2W/Z2Yv/iALYu/Zm+o2+vfZBV4KxzI8LZBWfNtfkQtRhLUJ3tMxZCMZuJ/+wJTEnxSIoW65k0NMeKUUK1SF39CH78ebucpybyzsRwMv+l8sdasxdtO3/gkGQlNS6RpplBHIs4y7B+NbzsV6RD8bj6GK06I0liBItQ74mEpRFRFYWcuXPJ/Ox/qOfqjxStXYuk16OWGjkzdSruI0cS9NqrnOrd59xRXlhfeQCnlq3Rt4jG0H+c417ARdybBEMKhHmcpuOwSFxbdqp1m71G3kDy8W84sKQFEn/SpEVTgqNao5itJJ48QNbZDHR6d/IystDqVYrzLJhKLXgH6fEO9MUrwJ+IVh2rXU6/nVckK9OOMm/Pq9zZ5bUaLWBYVVonA7rCUhRFqVXZ/+w9f5P+wTtI+7NQfLCtjudnwO31uwid+DSyAxPc3DN72Rd7CxqzGyEud+Dq2pLgTmPrfJmDy/H1DyCDM8j+lxaKqwpVVdEVuOLevNjOkVWdGHMrNAQiYWkkClas5Oy0aZds1/r7E/7dd+gjwjneth2FK1ZQuGIFAH4P3E32nG/J+nMnsBOAsDeScLvlsWsYeeUST9vePVv1CiJi2FC7tCnLGq6/92bmz9zI/iWe7CcL2HLRHrZBvRq9M9YyV5AtuHjnknb0/HosufS7Yx0d+1Uvnuf6fULOmtv54Mgi5p/6i0CDGwWWMp7o9DiDWkyxy2s7L3DQSNzWfUbM/pV07DqqRm1kbl5A1v2voTqD+zv3EjbuWbvGWBsp+//meNazSOjo2Hwu3s26ODokdG62Hq3IvR6YbjDhpK9e4pKbXYTWqscr1LMuwqsaMYZFaABEwtIIKKWlnH3qqfLHYXO+Iene+/C5awp+jz+Oxs22TonbkCEUrV0LgO/DD+H/5JP43j0VS/wRJBcXEiZNIeurb3Cd+HCtxovYw5AH+/DDm8dYs8qF+APz0GCi+x3X4dWyZgv4nefm6cO979xIelIC2elJZKekU5xXhnegG50HDsFszsPVPYSs1ERc3Hxx9fBAURTyMtP47a1dbP9NxljyJ2179sbNM7BK5/RxCearG9azPvZ7fjz2E6XmYjJNJXx/5Fu7JyzugU1QAdlc8wGcVtO5svYKGHyj7BOYHRRnxXMi4yVUnZlOTX6qF8kKgCRLHAk7Q9ukcDKyUggLqd7v7FS8rbBhRHhwXYRXJRKiNL9Q/4lZQo2AajaDquJz1xQif/8dt759iT5+jMDp08uTFYCAZ54pv+99880AyN4B6LsMQte6J4HT7qf0rImSv2Zf89fwT+5hoTz06XV0iIonK9eJkxktWDorxi5tS7JEUEQUbXsMYMCNNzNq6mR6jRqHk7Mbbh5NkCQZ/5AoXM/Vf5FlGZ/AEMY9E43etYQ9f3jw/fQDLPhgDrFH1lb5vIOaT2HO2JX8PGELN0UNYk9+NgeSV9jlNZ2ndbfFnHOo5tN5/a+7Df2Dg1HdZDJmf2ynyGrv8M6nkVQNXSJ+x7dl9WeM1aXQjraBtumJydU+1qyzDQQ/vS/LrjFVi7gkJDQAImFp4FSrlTP33AuALiwc53ZtL7uvU9Mooo8fI/r4MXQhIZc8737HU2gMKoVLr+3U38uR9Tr6v3Avd3w2iU7N4rFYHNtvHRTWiilvTGT8C01oM7iE/BRfVn6msuTrH6vd1u0dn8dVhs/3vWfXGEOadQSgKCOlxm1odE5EPPyu7YHOsZ2wZmMJZ3b+yP4lD1HkfIgg+Va8m9VNMbzaMHGuqm0NVj3u27ELhe0SyN+iZ/veg3aOrGokSeQrQv0nEpYGriwhAePhw4R88D4+d0yuVVuSRoNbuxBytyaiXuMKoVfj4edCscWTvFOxDo1Dq9UREtWSgTeN5+53xhDWOYUze0PYv3FVtdoJcAtnQkQv9ualczr7gN3iK83JAMDJrXbjIYqP70WTreA1YYI9wqqRuM2fs2lLJ04Vv0aO62o0Zk9C2tSPQeH/1K5HD4xSGaVJedU+VpIknnhgEiWueWxYs9/+wVWVyFiEeq5WCcvbb7+NJElMu2iw5+zZsxk4cCAeHh5IkkReXl6V2vr888+JjIzEYDDQs2dPdu3aVZvQGqRjqQV8sSGOMkvFZEG1WMia/TWZs2ZxvENH4kaOIvHOKSTeOYXTo8cgu7ridt11do2l8Ns37dpebTUfPQS9XMpPH5zhxMKljg4HAK1Wy5j77kBrKOLwxsxqH39X5xdRVYn7V96FyWyfGSI+ES0508IT55+XUVCaV+N2yrJtPTTOka3sEld1leamkGD+EHdjZ1q6zWTggOMMHLEPjybRDonnanJyMjGoekx5tn/H3LMZnDgaQ06O7e/i9OmTrPnuNxRr5V8EXPTOGJqXYUgKoLTEQeteiYG3Qj1X44Rl9+7dfPXVV3To0KHC9pKSEkaOHMlLL710mSMv9euvv/L000/z2muvsW/fPjp27MiIESPIyMioaXgN0rjPt/LOiuMsOVixOz/7mzlkfvghWZ9+hlpWhsbXF21QELKrK4YOHYj4+acKY1VqI/DD7wAwHnTgN71KOAcEMOkFWxn44sxcB0dzgayR8QjOw1xa/UHKge7NCHJyIstsRZbtUz9EkiQ87rsbnwKF7FOHa9yOqphtdzSO6YRNPbQEgNadZxDWY5JD6qtUR36erTBg61MhbHjzF4o/O4Hr93kUvXuUbW/+jn52Oq2PBbH1xyWXbaPvsDZoLToWL9l0rcIWhAalRheoi4qKmDx5Ml9//TUzZ86s8Nz53pYNGzZUub0PP/yQ+++/n7vvvhuAL7/8kqVLl/Ltt9/y4osv1iTEBqfIZCnvWXnmt4P8tjeJHpE+HNt7FCUmjamDx9D7zZfQenvXaRyagDBcm7pgjK/+4MG6pFqtJO+MATzJz3ZcRdDKNGntzqHlnuRmZOId4M/hlHV8tudNMox5lFjNuGgNhLv6cUv03fSNurn8uLicIxRayujm5YdOY7+6Jlaz7Ru6UlLzXpuSeFuyIzs52yWm6lJVCygyitnokPNXV+vWHVgb9iutkkJQXGBvswSat2tHdmIKJceKiZPP0iw/FP+TBlRVRZIu7c7o2qwjKzx/Ql4fwkeb/6YsoID+o9vQrWNb9PU8YROEa6FGX58effRRRo8ezdChta+PUVZWxt69eyu0JcsyQ4cOZfv27ZUeYzKZKCgoqPDT0CXllABwU1fb6rc7TucwZ0s8qWUS+wJaMl3XntMnEq9JLGU5Jkri69fvdO/Xv7FmnSfe+jQiO1VtOvG10q5vd1TJyouz3+e6nzoyafWTHCvMIsQ1iM5+0QQYPDiUd5aHNr3BFztsM7VKzQXc+vdt5FtVRjez37iM9ISjGGZ+Sa67jFeLyw/AvpwTr93K4eEdKf1wMUqEMx7hta8wXBMRPe9GthqIjfnIIeeviSGP3kqTt/sz+KlJjBp/C02btmTA6NEMeHgiktbWA5dtKKg0WQFb79jUp4biObIYbYdCdBme7P86h4/fW3AtX4Yg1FvV7mGZP38++/btY/du+6yCmpWVhdVqJTCw4odQYGAgx48fr/SYt956i9dff90u568vErNtCcuLo1rz/s0dKzwXc+Ak932/jwd+OcS6rm2RdXX7bUt2dkbNK0LJS0f2DDhXBtOxjh2WiPQ4xuh3H3V0KJfw8Akmz7CJoNxoAnsk0C6wK6Nb34+r04XeMLPFyEN/D+P7U6swmR9kc9p+TCpMbzeeCe2eukLr1RN7ZCs+RhW3T97C17dJtY49/ekTKL8egiY6rK1ciPjoa+RrtKTAP2mdXTCoYeS7bsVSVopW75ienpo4fHAvmgWZOCl6YoNS8cl1JajMiyNtztLrhhFXPLZJUBB33DgWgJziXGZ/vBT3xBDWbN7B0P71ayq3IFxr1ephSUpK4sknn+Snn37CYLDPeiU1MX36dPLz88t/kpKSHBaLvRxPK8DDoMXX9dJLA+07teS9kZEkOPuyYvHWOo8l4OmnATjRayCn+7dHKc6v83NeiTEnlyKLN4Hhrg6N43I0sobQQU74FTfBfckkTn7fhFmfLCUt+8JAXJ3WwJuD5uCh1fLt6a2YFIVoV1fGtXvCrrHk/vYbAOFNO1Vp//wT24j7ahqJ3/4fxjmrUYaE0GblPtr9tRf3po4tzKa1eAEQt/1jFIvVobFU1e7tm7H+nopJZyGheQ5uhU4UeBphSggjptyGp1fVL+n6uHrj4mP7TnnipxLemf4rZzPT6yRuVRVjboX6r1o9LHv37iUjI4MuXS68kVmtVjZt2sT//vc/TCYTmmpWSPXz80Oj0ZCeXvE/Ynp6OkFBQZUe4+TkhJNTzdbtqK+2nMqidzPfy3YX9x/WE//l89l2IJ3rJw6s01jcxk6iSUke+fO/p/BYHmX7NmHoP7ZOz3klWz9fhEIkTbrVzxkiAHffMJHt4Qc4tq+U4kI98jEflizfxP13TCzfJ8izJctu3Y3FWoaTzsWu58/LTWXn/bfS7HAmpzr7Ex0WcdVjjOmnSRlnq+FTBihRzjSd8TWypn4UwG7X+322HexPsvUbPI52ILjDaEeHdFXuS0oo0lkIvb8rnULDat3e/XePZ++pGDYtPoJnUhh//t8RbnjDSljApXWUBKGxq1YPy5AhQ4iJieHAgQPlP926dWPy5MkcOHCg2skKgF6vp2vXrqxde6FiqKIorF27lt69e1e7vYao0Ghmf1Ie/Vv4X/LcV38c5bqXVzFm+jIynTwoUa7N9yD3Wx8m8N3PATDu2XKVveuGalU49dcyTp4NJTosiaBzK03XV707deKee8YS1cUHgOgWTS/ZRyNr7Z6sAGx98X4CTmSS8NgYhnyzuErHxL8zzRbT7d2I3LKMdsv34eJ/acyOojG4gGJ7T/GO7ObgaC5VUJRHYdGFsV47l67GTXHB2s+dYDskKwDOBicki4RLSgAAheFnCfT2s0vbgtDQVOurlLu7O+3atauwzdXVFV9f3/LtaWlppKWlERtrK/AVExODu7s74eHh+PjY3siHDBnC+PHjeewx2yJ7Tz/9NHfddRfdunWjR48efPzxxxQXF5fPGmrsZiw+ilVR+e+yY/SI8sHfRY8VlcJcEx/sjEePhNYi08Zcyl2Tu1+zuHQtumAI0lG4YQNe9htmUWX75y1k+25fwjzP0Ou+mi3k5wjxRzOQCeDg/lP06dYRuY6nBhcX5eK17zRZncIZ9VjVKudaLSaUPQnQN4DoV3+o0/hqwmwq5OjWl0BrJUI3DYNH/RloveXoRpx+ySbA7ANIpMgmivVG/E1unPZMoUtv+/2trt6xhcM/FGD0yGfM471oHjLYbm1XIIrGCQ2A3ft+v/zyywoDYgcMGADA3LlzmTp1KgBxcXFkZV1YN+PWW28lMzOTV199lbS0NDp16sSKFSsuGYjbWPWM8mHhvmRKyqwM/+gfNRgk+Ovenmx5ZysSWjr2bFd5I3VEkkBV7LMQoqoo5B3ej9loIaDH1XtLXNxtAz69PM24NKC/hbFj+/Fr5kbcDgTz+/JV3DJmZJ2dy2q1sOGBCTQpVfG678EqH5fyx0fIGWbcHhheZ7HVhLmogF0bb8TonAiyhqCySTQd8JCjw6pg25513G4eSqzPWfRtvTEdz6VFZignApLp88g4nA326UFbvX0bR34oxOxTwOPTx+Hmav+eufNUVUWSxSgWoX6rdcLyz3orM2bMYMaMGVc8JiEh4ZJtjz32WHmPy7/NLd3DuKV7GMdSC1iy6CTJx3IJau1NsslEdKgH0S18iSGXTG0YRcmZuDW59NJRnZE1mNLsM8U5Zt58Nu+yjUvquPFbetx/I3ovn0r3VRUFvattZoixrGGtIBEZ0gSNu62mjqtb3c5uWfnyVCL3pZH32v30va7qZfRdItpSBOib2OfShb0c3PQIRudEgrmT4KY34l3FwcPX0jO3/R/rP/2VZtkh0CKE8JFDOX36BIOa9UWW7fO3unrLdo7+XIjRL5fHp9+Iq3PdJSsAiiISFqH+a1ifBI1cdLAHQ5v708qs4bFb2zLr6b48fmt7AEa9PBSN1cjOL6q+OnCtqCpl2//EUmBEkqq/rlBpbiGWkhJUiwXFbCE3LpGdu90B6NI8joNxkfz+f8s49fsfqNaKM0AKzyTy89MLWb5Eh7OcT8dRbezykq4VRVXQHQ+gKPIsowfad8mEi8VuW0HQsr0kjulI30lPV+tYc56tirQ5Oa0uQquRguTj5Bu200S+nzaDZ9TLZAVAo9cS0KsZOlVL3J4YNBoNLVq0sVuycjo5iWM/FVLin8lj1yBZAVAVFVkkLEI9Vz+mAwjlfIJsU3c1uopvfu5RoXiX/E1mSt3MjlKK8sn5z4NY8kpwGzoS88kDpP2wGSSV0CdurFZbZw5nsOR/tkqpOqkUs2rrZXCSoW1kMr2euY+CGXOJTY9k1Ro4sfc7Rr40Ea27J1aThSUfbMds0XH9mBLCR4xF4+AVg6tLlmRkZHTOdfd9YN2sVwj8bCE5wU70ePatah+ff2AdqqwScot9p1XXRtop2xpRIa0mXmVPx9OuygdckLX2uVx6MT9vb8p0RjSuKm7XIFkBUBWQxNdXoZ5rWJ8E/wLuvrb6Nu/NmseYcX3o1qZ9+XN+/hriinxRLFa7v1EWzf8fmX/ZlrbP3XgKAJdIV8J+XYns6VuttvzCLqwUHOmfgs7ZgEFnovt9N6L1ss1wGPL8LbTavI2CrBK2bQnht1f+pl0XHWV5OeSaWjJhioHgPn3s9OquvVLPPDhjIPZsAs1DI+3attlswuXbRcR3DmTo3KUYnKpfn8a88ThqlB5Z77h6Sv/kE9WLpNOzyI7bjntwC0eHc0VpAXl4JLsQ0Mz+l9Q8XN0I6CNTsCGYLXv30q9rV7uf45/EGBahIRA5dT2T5C6xuFsCOeY27Pw0k3efXsgbb87hp0V/I4X4Yta6krb9qN3P63zdpTUugt76sNrJCoCLpxPjb7clVEEt/Bg0/U56P3tfebICoHV1I3LkcDrccSNjJ7vjbLCwZbsXO461xEkuIbAKg3Lrs76TmmIocef3j+276rjVXMbaL17Gs0ih2SNP1yhZAZA9XZEKLJiK6s8Co74RfXA1tiOh6EPyk484OpwrajW+NwoK6p/pJJ9NsHv7k24aSYFXBtt/TqSktNTu7f+T1apiNSuoqpguJNRfImGpR8oUhQeOJHKwWRcW9HdnR0sn0Ct4nA0lb6ULx07bei7yzAl2P7c1zdamV58IvLoFEnBrf5w6D6hxewHduhHsksDuHVrKCq+8CF9o/77c+M5d3Pt2d257sgm3PN++Trrar6W+HbtijMzAUOJutzYVRWFX/65EzFpKYnt/ovvdUOO2gp95BU2mSuKH1Rv7UpckSaLzdXMAmZhDj1CUftrRIV1WaGg4prt8kVWZrK9iSE5KsGv7Oq2W4VPboS915etv/rRr25WxmKzsWhLP5l9P1fm5BKGmRMJSj9x5KJ4zxjLuCPZFi4UN7Ut58s1xTHmrF/2nNcEzcD89d75O/vMvkJtp3+UIyg5uA8Bj3G0E/7gB39dn16o9rYsr4e0CMFpdKT1btQ8evZc3vtEt8Yi8epXW+u5sRhrOCYE4dSmyW5tbf/kArzwLic3d6TdnUa3acomwTY+3pNSfQbcATq5+RHg+ismQTHrsSkeHc0Utotvh80g7rJJCzuwjnD590q7td2vdAZfexWiO+LNy+2a7tv1PNz7VGe8gF0qLyur0PIJQGyJhqUc25hbSR93Mnb75zAgDs9aPRUlH8fH0okPrltzx+jOE/fklbiUqO154wK7dt9omkQCUbFxqtzZD2oYgYWX7r4fs1mZDsffIUWRVQ9eerezWZvGPv5IU4cKg39bj5lH9S3XnqapKwi+2gbqhj71mr/DsJqi1rfBaSUGcgyO5uuCQMMIf7YFVVsj4PoakBPv2Ct19+zhKfHM4sCCd9Ly6u3wX2sobg5sOTR0XORSE2hB/nfVIe/UAj/IxWTHjaLpxMjO/eJf0U8/yw8aRWBXb1N/AiGiSbupF5LYEFo7pyN8v30n2ucs5teFy4yMgqWQtO0zpiu9r3R5A8q4jqGiISw2+ZOpyYzegazeskoWdew/bpb29S74lIr4YhvTFybnmi0BmHz3K66+/zi85vhiHNcOzXX+7xGdP6SdWAeAT0DCW5vAPCCbknk6EGP0onR1HZlqq3drWajTc/EBfDCY3vv14FSZT3fWAKFYVWSsG3gr1l0hY6pGPg46V31ejLPRM20c7OZ4Q6ylmffU88SknABj1+rck3DWIyGQzzRbuYd+379vl/FHffgJAwrS3UC3mWrcX0i60/P53T/5F4up1tW6zofDy8MASkYN1pxdrt2+vVVs7fvwQl+feI89VYvAzH9aqrdOb15ff93n2jVq1VRdiN/+PuKI3cSvpQHDn8Y4Op8oCI8LQPBSBVVIwfRzLqk9/RrFWv35RZaIiQmk1yRWXdH8++uBXyqx1k7RYLUqdLyMhCLUh/jrrkWbNnkKWnZCLJTSZIOfb1nw/czKY0o3H+fmFp9i2dyWSJDFq+iw67z+MSQvmVPt8ozP0HoFrK1vl2bR7al+yvcmgwTwyayDhPskUW7z4e6GCavn39LQ8+MQ4Sv1yOfxDAWmp2TVqI+PsKTxnfg1AZvcotLVYSdmYm8u6hGQADIZSfvjhd9LSjrF9+8/k56dU2FdRrGRmniQxcT85OelkZcWTnHywxueuisTdP3LG+D+8ywbSefB3divEdq2ERkbi8aStyGGblDBKS6882Lw6RvbvT+SNOtzPhPLx7B/rZDaPYlXRaEQPi1B/SWojmMdWUFCAp6cn+fn5eHh4ODqcWkt//z2y58xBUiVSPiujddQ89J5N+eK1h9EUWbjpjbdoFdkRgK292lEQ4MaoxTvscm6lMJcT3W31T1qsWIg2svZVZlM3r+ePn2x/ZiOuN9L8hutr3WZDkZR9lkWvHMGpfx4P3n7LFfe1Wswse/pWdC6uuHfojM7FFZeXPkajgGn2TNr3HYemFgnL32+/w96SYqYMHQZher77bnmF5yOjFBQFiouM5BdosJgvLVL4xBNT8PGx/4rOeWcOsffkTXgZ+9Bx6OdoDTW/7OVoG+YsJDjOnbCX++Di6mbXtn/8cTn5W5zQDMzgodtus2vbP722g4j2vvS7qX7XwBEal+p8fjesrzD/EoHPPgcThmNsqVCWE4lvcFv8/UO56+X3kZD47b0LAyVLnTVEnsxny++f2uXcsrs3IY/cCEDOR6/Ypc3g/oO4d4Zt8OnhTSmUpibbpd2GIMQrCLOhlMy4q3/bNpcU03zVMSL+3IPPG1/j/uLHSCrkvnQPnQZMrHGyUlZUxLK33+ZQfh6hJhNRA/oTEVFx1e+mTcvIyS4lP78Eg0FD+3Y+XH99WyZO7MTIkRH07m3reZs792MsFvtfkoiNeR99mT8dhs5q0MkKgFW1YlD0WMwWu7c9efJIaJOHdUMAX8xdYPf2Rf+KUJ+JSrf1SJm1jAUnFvDjsR+J6BpE6NlIXhi7CCcXW3nuiNCWBAzuTv7yPcya+zIPT51J6HvvkfL0Mzh9NIdl27ciJaVSZikjYMpUet9Ys1VuPR7/LylfLCJ75TG8Tx9H17R1rV+bISiUliGrOJnSlC3fbGDY/91R6zYbgtnf/YGh1BfnYNMV9zNbytjyxaucH/VzfHQbNJKGtndPo23b2lX83fD1N+wyGgmXZQYNGQKALMvcf/9Yjhxdz3UD7sfJyfMqrYB/wHcs/gtWrnqD0dfPrFVMF7OWGSnU7SVYOxmdnVY6diSL2UyqIYsILy+7ty1JEg8/diNfzPkdZacff0dtYMzAgXZpW1VV2/LsglBPiR6WekBVVf4+/TdjFo3hvT3v0cGvAzHFx/ltUApbjq+psO+EGx8mP0RL6YqDfPzh4wS27oJ89634ZpcRtfQQkYcyaXk0n5x1q2ocjyRJBN05CICUZ6bV5qVVMPgpW6Gz7Ox/x59dTn4eBUegMCCNR+67/OWg9XNeJ7ZdR0LnriaucwBF/53Gje//zg3vL6DZP5IVi7mMlLMnqhxD6v797MjJpjVwz5tvEjV0aPlzoaFdGT7s2SolKwCdO00hKKiM3bu0bNr8TZVjuJrkgwtQtEb8w4bZrU1Hci7QUSbbv3flPFmWefjemyjyzOLglgS7ti3SFaE++3d8ctRjBWUFPL/peaZvnk5b37YsGreId697l9k9PwMgPrtiXQcfr0Be+2AhxjY+KLsS+N/7TzBgyouceXAU1nPvNmfa+dP5kVdY//1/MZuv/M3+crxf+gKAkmOJGHeurvkLvEjipj0AdOxZf9avqSulRiNffbAUvdGFkbd1ueK+RUttY0nyX32QMb9spPuEB5Eq+aZ7fP0iTrXvSP6QG1k6dTjFRblXjSPj1CkUjYZmLVvW7IVcRJIkJk9+CoB1a5P54osXOXRoWY3bU6xWjq+fSWzBG3iW9MK7Wd2vmXMtqM0NhJcEkZ1Vd3VTZFlG62obKGs3DX40o9DYiYTFgXal7qLvL31ZkbCC9wa8x0eDPqKpp21A4w9bvkRSYGDbEZccp5E1vPza9/jd0AfXYwWs37aInlOeJc/N9iGnKTaSf+Nkgv77AxvHDSA98dglbVRF+PTb0Dpbib/7cZTc9Jq/0HO8w2zFznbvkLEa6359FEeav3AlHhnBtLnNk05trnxJTdLpyXfX4BnelIXjuvD7A8PZvfqH8to7ACtnv4L68Evlj5vuSCJh8/LKmqug/U034V9czM4j9lmbx93dn5tvHoSHh5H0dAN//LGL776fjqJUv0fhyJrnOavMI9hyO11GNrxZQZfTZmAPymQzpe+fYM3sX1k3+zfWfD6fnRvsO61fkiVQ7NcnooLoYhHqtcbxDtFABbsFl9930lSckbFO2UtfqT3NQ6JRVZW03cc4NmsFWQcv9LjcMekFirxh64/f4eEdRO9tB0ieOhTZaCZ2SEti7+yH35kCksZNJG794mrH53rXa4S+8xYoEsmTL10csbq8O3SjY/NECk0epO/ZW+v26itFUcjaZ6EgNJkR/ftddX+PkSPwLLTCfS/Q5kQpkbuScHv8v2y8rhN/PTSaxS9MIvzDhcR2CSRoyxpOTbT1RBQWX72HRZZlPPR68uw4NqFt2+t4+um3eeGFaTRvoSH+tBPbt8+vVhtlxflk6P7ExzSENiPeQNY2nuF0/r5B5A/WUqApwiNVh0uaTOukUPxWqRTkX/3frKokWUU223HNLVUVQ1iEek0kLA4U5h7Gj9f/CIBJuXDpxlRSjBUFbWYpilXhyOxlWBZm4X7GldKfkzn6xhIKUzLRyBqiBvTDLQ/eemAc2/avZtiLnzF4/X7Gfv4XY1/+Gvff51KqUyl+7AXOZla/1LnL8IkAFJ8uRjXXrphcSXIiB2Nt6wQVZhtr1VZ9tnn3XlyKvWnWzb9K+/e87UlMOtv9tDfup8vewxjffwFrywi8j56lxV8HKHCT6fnBHLz9QrGk2y41FMQcqFL7qVYr4aaaXRq8EmdnLybf/gpubqUcOHC8Wscas88C4Bc4xO5x1Qfdhw6i9X9G0OO1CfR6dSKx1+XjpOg59Pmaqx9cRa5REp45wcQmJtqtTdHFItRnImFxMJPF9kHiZNRx+7yb6f9tHx5b8DBmnYrk5E3Ky1vxivcgz5yB/4udKWhehFORE2c/3kl+Qip33PocQbcNwVCosuWTz8nIqVgATKtKlLjr0VnBmJVZoxg9e9jmrpQsrt1Ay7KiCwsBhvVqX6u26rPIJiFYJQs5qVUrHObk7EaHA4dpfuQQg255GlmjofOYqQyb8zcDNh0gas8OOm/ZTUBwMwB0PrYpxmEDRl61bWNhIRZJRtXpav6CrkCSJLy8tJSUmLBYqp4UJZ+Yj2xxIbjdmDqJqz64+BLXwFFjSPBKwyzZbzDuLWNHoEoqK7dstVubIl8R6jORsFwjMRuSWfLpAfLSSyps12v0ADyxcxox0nFaaJpiVs1EmEPoXNaSHPcs8rxz8RrTHCcvN9refz0+90ajRUfSrO2UFRYzefxTtHloEnqLzPKVFdcBOvTBa4Sn2OpmNG3dEwCzsRRTadVXEQ767FcAUt78GNVS8zdcz5bRtI+w9fIc/XNDjdup7yJCQzE2Tce4x5XDsVVbwVfWaNBpKk8qDG6e6C+a7qs9GseZKFdaD55w1Xb//uADLFoN3Xr2rFrwNdC9ey+Ki52ZN+9VrNaq9cLllm3FtSwarVPDn8ZcVSZ3M05m+73lerl5UOqTS/7p2i+jAdDwS4gKjZ1IWK6BEztS2TT/JGeO5pCdUjFR6BTQidcznyag0HapZNatXzPv7l/4+76V3P3si3R4eTztXriBJoM6lh/j3SoMt3HheMn+xP9uW6cmMsRWmC3rzy2UmS5cbmnz0pvl91eP6MrK8X053KMrx3p0Z8PAziydPJg1n75A4rFdl41f9vQl9Pk7sJTIZP/f3TX+PUiyTP/n7kbGws79Vbtc0lDd88BodIoTSxfZpwLxefsWzyEytgjr+OGVziQ6rzAlhfmvvsphoJPBQNtx4+wax8U6dhzLiBGtSE52Zu/eK4+VUlWVHX9/gNE5gWISyUvPqVBm3lhYyqYFq1k9bzFZCWl1FrMj+BWl41zmzLYdH9utTfdIGad0H0w1nA1YgSo6WIT6rfGMdKunclKL2fDzCbwCXS7pXQE4czSb1NgIJvA0D30+sMrLuwf1jSZm1SKcThrIjU3i0MkLH4yJ6bG0CG8HQFRUZ5aNbIslJxtdqRlJkoi/oRPOBnfMqSk4x6YS9MViSmYtZnWYM863TkQqLsEQHU3Hwbei1dq+9Xvc8zLFGzeQvXQ3vjOtSJqaDfaTtFoUtOilS38XjYmfpw9pgSdxSfW2a7tZs2dDpCvD76184ULFamXFhx9yIDcXVZIYGBjIgAcftGsMlenZ8xZWr36dvLwsAIzGQoqKspAkFUmSkSSJEyd+IzNzKS6uZ8jNDebY0QFs3PMprhho4R9J05bNWLdtAwVKCRpkdsQfYMKgsbQd2LnW8Sn5OWCxIvs6JlG2piTim9ueVV6r2HJsLX16TbNLu106t2bP3gxWbN7CuMG1Gw+kioxFqOdEwlLH1n1/DHcfAze92I2vp23i4JokmnUOAGzfNpd+fggAWaLKycp54VN6kv7VAXJmH8U5NxMXrQfh911fnqzY2pUZ8/HvV2wnPzuVIyt+xu3TeXi9/yNWCTQq7PJ4i9ymflgiQ3AODMEnOQ/XMokF8++kU6+7aNXs0inXVeGhzaLA4nfJ9tVzj3ByZzoTn+9KUNOqFTOrz1zcDHik+5OWk0GQT0CN21EUhYQ969B5++CfWEBKj8hKF0Esyclh6zffsKukBA9JYuqDD+ITHl6bl1BlsqxDlhVMpmKyc+LZvXs0Ot2l3/q1Wn8CA16lX89b6Nsmk4yEVBLiEziaEcuBzOP4ajy4//Z78Q72ZdYHn3Fw7/4aJyyWhNOUrN2BKdVKWZE/IBH4eBu0oWG1fLXVV/jbWiQphLKmqewuLaO4MBVX9+CrH3gV3TpFs9p/H5bfA1isWcsN19UiaVG5Yq+dIDiaSFjqUElBGenxBQy9uw2H1tvWz2k7IBRLmRWNTkZRVBRFxc1VS3GxhbJiM3rXqg+O9GwWgvyElsRZW+niO5S2vv1JyTNRkJ+Lh2fVv9l7+gbTZ/IzWG56lOyCdFw9fIjfvJycZb+jj03Cb20MbkUHUCRICpL4vOAguVuepd/W13hz7M/4eFdvMTyD3kSBBYqSk3BrcuHDIy0uH4BFH+zDr4kbZSYrnn7OeAW50G1UJIZq/G7qAyedbXxSQW4JQT41b2frJy/h99VfmACtDO6DBlW630/vvstZg4EIs5k7XnkFnbNzzU9aTVarFVWF7JzN7N69F53OhKvLgzi7BIOqoqgKFksRXbs8jCzbeueCo8MJjg6nIz0ZUVhK8uF4Iru0QOuk48S6gxQqpXQIqH6ipyoq+V/8QlFSGBCIk1sqLkEZlKQFkTN7E37ThiN7X7ueFmviSYpSI/BolU2SxTYofsvBbxnR7+Vat63RanjshRuZ/f4ykn4J4L3Nv9BtVBQDu/QUyYfQ6IiEpQ6dPWmruZCfUcLupQn0GBtF864BfPnYBgACozxAhaBQN2JP5mEurV7CAuDeJIB2/x1PbnYmJ+euoeXOJhw9tI6er06o9huW1slAoL9tLE37obfQfuiFcvJWxYpG1tAeGGQqYv3OD5kZ9yu3/DmOt9o9QrdOdyPpqlbB9vqnBzJv5ikWfRzDpLdC0Oo05KUXU5BlG3uj0cpkJBYCkJdWQuLhbI5vS+W+DwdU6/U4WqtWwSQcV1n5yY+4Pz2e4Mi2NWqnwKDiB+RMn0pU/+tp37TyGVYlFgs6i4Upr72GRq+vReTV99Mn32K16gnJa4UuaCkmcy969Xq+yscb3J1p3tu2MnjKvtP8tWkZwc5+DLp9VLVjMW3dQFFSGO5NE3Ed3hNtpK3XwXX/PjJ+dSbr3d8IeOuRardbU/kLtiJLgbjdNIoJJ51I3PMc/zn5s10SFrANvn36lZv58a8lFG3VcfTrErZ7zMevvY7Rw/oTGhRol/MIgqOJQbd1KPl4Lt5BLuxbdYbovsF0uz6S0sILI/pdPPR0HBxGp2G2bvtTK2teT8Hb15+BT9/K8YizNCkNIN+OBarAVl33PJ2TG8MHvMqCkT8QjJZ7js5i4A9deeb7vixY+QTxiZtQrdZK28nPiSW+wDZQuKDIwI//twOz0cKK2YcBGHZPWx745Doe/XIwj345mAc/uw4AU4mFLx/fwLIvDxG7Nx1FUez6+uqCuuEEkmql96ZFZF1/E3/dOYid635GUasW+9mT+0mO3U+bXtcDkLd1EyGXSVYAhg0YgFmrZdlHH9kl/urQKDIeijMtU0fSYv0surX6oEbtpMWeZc5fP6KVtYy/dSKaao6VUstMFKw5g0aTjce9t6ONbFG+vXD5PgCcWoXUKLaaMO3YTEl2czw6lyC5uhC09AXGFpSRr5HJrcZMvavRajVMnXgjz743gRa3GdB5qhi3efDH64dYsmF9ldpQVcQYFqFeEz0sdejolhQ8A5yxmhWatPJGVWH5l7YxK8Pva0uLbrZvPieWxgPVH8PyT7Is03xoF5iTztEv19PnxYm1ewFXERTchblTdrHr+G/siV3G7tzjvJW6DkvaelwVhXB0jA0dwA09nmHDrk/4Knk1Sec+f+7WvI2bQaE4D2ZP2wRARDtfWvao+G1Qq9PQ96bmbPsjFqtZIf5AFvEHspBkePDTgWi0V/+dKYpS47LviqJgtSjo9FX7r5Ifl0JhbCKZScUk5rfEuyiOZmuXsO/b9/BbtBaPR/7Dysj38Xr4AQKj2hHRtheaSsajAGTcNBlDmUrckFY0A+SkK8+aiR47FqedOykoK6vuy6wxVVU5sn4fZ4syCHEPoChIh9cRBeOXp+DtoGq1ZTRb+HrbHrJd3Hj2gQdw9nardjymndspMzXFZzhI5/4/qVaFrHcXYipqjne3VFxvuvxClFWlGo0Y167GmlOAOcOIS7+2OPXsVXEfs5m8pWfROUnox93I1lkP0U9J5WzU/0Hxd+xOOs3wlh1qHcvFNBqZ4QP7MHxgH9Jzs5j7/moOL5MYO7AqL0oV+YpQr4mEpY6Yy2w9DM5uOopzTZw9lYd/uDsZiYVcd3ur8mRFVVSCOvjCknjys2q/vk54s+akkE54XgCKqiBLdduJptXo6NP2dvq0vR2AksI09h79lbisIxzOOcKHKet49y/bN7whei+eiBhGsG80a3eBpn0J/Vp0JTEmGxdPPX1vblHpOToNDafT0HAURWH/qjPs+PM0qgJfT9tEx6FN6H1jc7LOFpJ0NIfmXQJw93XGVGJm19/xHFpnGzv00P+qltxcLCU2j8UfH8BqUXBy0dL+uib0HHfpeJ1tT31KdpEe50AfTmSdH0wso7MUM/L/RuHtG8KQ5z5CecrCoUXf4PTVbLxe+AQTsNtVIqdTBBG3TqXt8FsBW5K0csZ9RJbZpvtGrbWtzqz273HFeDd89RUmnY5WkZHVep01lZ+ey49fzyPTko+P1p0RE6/n49R85jQx0jargKZrt/Faj440cXe9altZJaWMXr+bxLAIOngF1ChZUYsLyV5ahixZcB5wYUB41tsLMBWF4dP5FC433VO+XcnNpGTJYoxJEmXF/qBKaHQluPfxwHnY8AsJj6JQtn8/kkZC0llRcovIX51KmSkM8AJAjjmLJW8zam4qzr07Ioc2peiX3zGbm+A/UcfRj8bQz7iLzZFPMHbgHXz794+sOL3e7gnLxQK9/WgzOIik31T2HI6hW7sqFGsU416EekwkLHVEp9fgE+KKd7ArLboHsvnXU8TttZVUD4u2jcBcP20DR40XLg9YjJVfRqkOWZbJkPLRIONrMeGsu3YDLwFc3IPo3/NJ+p97nJGym9Ux39Op+RjaXjSraB3LQKunTd8Q2vStWhe9LMt0HRlJm74h/P7OHgqyjOxbcYbj29IoKbD1KmxbWPnyAwvf3YvBVUvr3sG07HH1b/6KorDy68NYrQohLb1IOZnHnuUJdBsdgUZ74TKFajYTl+ZCgWdTyLpwfKg2lVFv34CTl/uF+LVaOt38EOZxUzm1dRlFRTnkbl6HYUcM8hMzWDbgR0b873e2L/6KyAW2y2ZnQw34P/UUyev+ZtCTb1823rKCArYnJuIPtJ1Ytz1rAKZiIz99/R355mIm9hlN22FdkWWZNhbgrJEjfh6ctFj4e/cJhitGBgX6cGurpjhXMrbm+8MneC21gFIXDwAmB1V/9oxaUkj2xwtRCcN3tDPSRf9GpsJQNHIOLrfYagiZ9sVQtPYAxhw/VLUZetcMXMOykQx6TMlWcja4otu2EPeuWlRTGcXHzJSVRlx0Ni0ajQv+NzlRsOwIppLmFMV6oMYCBJN3IAOJBFSa4NYkgeNxp+lk3MXBPp/Rf/gUADyU9mxJXQc8We3XWh0j+vflf3+uZOe2rKsmLKJunFDfiYSlDgVEuJOdXMSgO1qTcjKPuP2ZRHX0w8PPNji1zFUHxgtTP71Crv5N9GpObothsdMeAF7VXL10e10LCOnO5JDul2yXkFClmr1FOrvruXNmH9bMO8qJHbZkxTvIha6jIkg6mktxvglFUYnuHUyLHoH8+Mp2Ms/YBvEmHcslJTaPdgNC8WviXmn76fH5/PnhfixmhcgOvox+pCN7liew86/TnNydQXTvCx+okk7HgMnRrP4jHZPeiwhdMr0fGYhv9ODLxq/TG2gz6FyV2rH3YbFaWPv5dJp89Tf7e3bBo0whqYUXYf/3Gm0DwgiObEvHMVMuacdiNPLDf/9LsqLiYi6jzNmZG8eOxdm98tdlT2ePJJBhyWNYu/60H3Hh33dyy2Bmn0qjuU7Hix1Dee3AcVbq3VmZZcRJk8ikNhV70XYknWVGSh5BZUaeDwnl4YwiIi5zieyf1OJ8zCePU3Y8iaIjEhZLOL4jZQz9+pfvY82wXUZzbVZE2a5N5K9JxlQYjlajxyU4C/eJg9GGDqzQrmnrenKXSeRst/WW6Q0JePfIQRcVgrVERuOhR9eyO5KTAQ9TEUVbEtF6KeiC3LFm56MNC8ecUUJxcRxxUT603/IJ+z0G0nn4hX/DENcwTpSsIT43nSjvuhsUq9NqscpmsFah50QVHSxC/SYSljrk7mPg+PY0vnlqE3fO7EP3MVH4hLiWz94Z8WZfLDN3kpBsW3Mm9mAW0WPKcPKo+QyPM0fjy+/XdNzGNWGHr3MDJrVEq5PpPjoKVy/batetel767fyut/pSVmYhN6WEv/93kCObUjiyKcV2aa57IE7OF/4bJB7OZtkXh1AUlR5jo+g2KhKATkPD2Ln4NEc3n62QsABEjOnL1OFmvn10JUVmCd/o6tU+0Wq0jHjiPXa2akfKij8xuHvT69EZeAdeuZ3EjZtIlGVcTaUEaHX0DgkhtGvXap27xs79+/mFXDrt2KKRyCnLYH7qCXo28WBjcg5WXSjjml3opYjLzuWZPYfZqXPDW1H4qU8HZIsOMmIpvEKNeFVRKF21iqLtmZhNQagYAB+c3JLxvbE5unYdK+5fYJsqX3AqnIJToNU64dMtBeexNyA5Vd776NR3EIF9VNSCIlRJi8ajf6X72fYdjFPfitvMZSYS3ulPtPUEwSmQhxvN7p1TYZ/net/NveuWMX3N58y/ufIigPawef8eXE1eNGt79dmHoodFqO9EwlKHuo6K5ND6ZEwlFuY8u5kR97fDN7TitfnRr/TEYrTw6wtbSM8v45vnt9Aqwg1TsQU3XwMt+4cS3K1q38DOHk1gW9L+8pH+J5JP0KpJK3u/LDup/Vc5vZOWgZNbV21fvZbASA/uea8fJ3elsWbuMTb+fIKNP5/A4Kpj4B2t2b8qkfT4AmStxOiH2xPZ4UKtjgNrkkCFwtzKS6Br9Tpcrfm4etb8v1TPEXfBiLuqvH94v754rl9HqUbDpJemo3WtfQ9dVViKTGxeug6NLBPYPLTCc6dTYylIv48CTMRm27b5AG5uHXFx+pG/Tp4muaiEjzKLsEpO3KMz80KfzngYDJSYLPiUqWzKKmBsJedV8vLJ+WIZxvwmOLlm4RFdgr65D/o2rZFcKu/R0oSG4x65G8WsRxvohtvECeVjU65EkiQkz5r1VO397R16WU9wsNmD6CL74BPanCCvioUSe4a3oJXzSI4ULaHI9BJuTlUrCVBVpWWlzPt2OYUnVWSDhqF9K/uN/oOqih4WoV4TCUsd0mhl7vtwAKlx+fzx3l5WfnOY5l0vfWPVGrRM/mQgmcdz2PbjcU6fKUKnlTmbbeTIiTwGJBTQ7qbKB6SCbbzF2Zh4/vhzES6ygdbDo9mzch+/fPMLQ24ZQv82l/+G6CiOel+UJIlWPYM5ti2V3NQSSgrKMBabWfFVDAD+4W6MuL8dnv4VF+VLjc0DIKS512Xb1mglkop9mX/nN3Sd2IYWN/apq5cBgM7VlcG9e7No/36Or1tPu7HXZuXjU0v2Ey9nMLrLULwCK1bEm7T6NsCEP1oCXN04UpwHwFing2w9vpcHU21jSzyAZV2a09LvwvEuTlq6miSOOF1YYNNqtdoGva5eTf6WMlTVA9/+OTiPvqNKsUrOzng+VLV97eHwmu/pdvIj1vrczJA7373ivq19W3DSuJSisjLcnAyYLRayS0oI8vCodRxffb8Q3YEQLCHJDJ/YBW2Vp4eLjEWov0TCcg0EN/MkqKknaafz+W76VmSNhKnUQli0D0PvaoNGZ/vG59/ah3EzL3zIWcosLPvPLjauSUIts9L+9kt7E6xWKz+8/w0JpanIqsSdN04iqnNLdh/ei3RWIsCr5iXh65bk0PfGG5/qUn4/LT6fzb+epFXPIDoMurRsu8VsxSfYlTNHcshILLhsm6NeHMiqt9eS4dqUE5vP1HnCAtCsTx+cdu7kz507iL5+VLXrltSEays/OAEnjh2nICuX/rcNQ+9i6yGYaxjLraW/g8VIUUEpo7zhOnczBhlm7VmOLmA4H4R40DusKWGel34wJ2pUIlXb/4eYpTtZuHs5ADeYuhHuVojXbT3QNqtfvYYlBTmkfHsHzvmxtFPTidM1o/cDn131OF9nWzXqYQvG4EELCqWTKFIh0a6j+G78DFx0TjWKZ8HK5ej2hKB0yuSlhy4d+yQIDVU9HuTQuNwwrROdh4fTqmcQEe38CIryJHZPBomHsy97jFavZcyMXkQGu7BlUwp75xzGaq5YdGzDLytIKE1lZLuBPDD5HqI6twTg+utsxca2HdlWdy+qltR6ctU8KMqTm1/sXmmyUpBVylePb+TAmiQkCboMj6ikBRvPpiF4Bdkuy3S59dqMJXHz9+fmPn2waLWsnzfvmpwztGMUnQJbk2zMYPOZvRzbeLD8uWY+bYiJP8MfwQP5+0wqbx9MxbfUwo/Hbmbr4c5oEku5pV3rSpMVRVU56SKRZrUSu/UIf+5aWf5c1GANfi/fWe+SldM7l5L/YTea523ljEdXdjZ7gojnt+NShWURnu47gTd7zKalew9K5NPolUDauI7mWMlS7vrjTcyXKb54JYdij5O8RKE0LINHH7i5WseKwnFCfSd6WK4RnV5DnwnNyx9bLQpfP7WJmI3JpJ7OJ/FwNs6eEk5+FpxctHQb1BpPbw9kjcyI57qx+L+72LE7A62Ljo6TbG/a1jILe04dxEN2oddNAyucr1vzbvyh/4PErYm8uPdF3CLd8NJ58cj4R+rFYNwaThC65pZ9EVN+X++s5fCmZAqyS+k1rhmJR7LIPluMd5ArYa292Pb2n5zM8CHEmkhIj8vPErI3q5Ptm7iSn39NzqfRaLjx4dvY8tta1hzeTECzi6alZ59GVSVc2j9D7ukteGedwd3/fZ5r348NC4+inC5iWXwm10dVspaPquJqUXDNy+OXXVuwSrbkfGxHf7yHVb9Ef10pzs8hPW4/BcfW0e7kLI7o2mO66Xd6t+5y9YP/4Ybo3twQ3bvCtmE/ZHHcuIiBP8Sy4rZvcTdUbXzL4YQTrPk4FouTifsfu77a/89VRUWWRcYi1F8iYXEQjVam3YBQDq5NIi0unxbdAzm+LxHluA4JiaMbNjPm0Y5EtWqC1kVL7rnBnuEXVYI9tvkApVIZQ3tduhieRtYw8baJLFq7CClVwnLcQhZZvBFjm5HQZnAbxvYai7P+2tZpaWjGPN6BHX+e5uTONMqMFjLPFJF5poi9yytbRsE2HiNTvXYL6wHs37IVJDiUkUnGq69y4+NP4OZ/6WrY9pKWU8Afa/cTG7OL7p4RBLe80DPldOy/IIE+rDnLclvTWTES3X0iskbm4GN+dHlvPY9+t5e/Hu9LB1930guLWBqbwIaMHA5YJYqd3ZFK02jlG0li7lGiPKHr+Efr7LVcTnrKGTIPrcaUcoQyswnXokRkUwFG2Zno0v00lUwoqsQG7wn0emgWLlVMKqrij5s/4aU1c1if9TWj5z/Gyslf4Ky7+iyf5X/tRIsvk1/oi3clPVhXoyqqWDBRqNdEwuJA/W5uQffRkagqGFx1nCjeQF5WNjeNnczab0+w9ONjOAfG4OSqwcXXCWOGlU3fHmH0qz0pyMilTLENTpTKKu+u6NG0Bz2a9kBRFApLC/lz25/Eb7VNez667ihH1x3FqrPy2GOPEegZyJbDWygxlZCSnYKPpw+dm3UmzO/SyyT20TDeGN28DAyd2oahU20L86Wdzmfhu3sBcHLR0mdCczb8fJzzywM1d06i5bA21zTGYZNuw/uPPyhSVWIkiV0Lf2fwQw/V2fkmfrqWs0Y9GtrywsTeKAV5nPjtDfaZ03iz95/cnLGaJwqyCDaf4YgaScS5WTkeTjpeHRjOq3/HMfazrbj18CDb4AyShDtOtNNZedrPmdv73cL2n9/haI4TA0bYt5ZQXm42J1d/gzE7Gb1XCE4ubihFmZQqGtrd8CReHp4c+HE6HWK/JFBSSVN9UGUNGbomGJ18cbXkcSRiCh6dbyQwsg2DvWuxDPdluBsMfDbmUT7dHsTsE6/T+4eRtPfqx5wb/g+9tvK37COnYtGe9MPQsYTQgOotiXCeooKsaRj/L4V/J5GwOFiJqYiTJ08SHx9Peno6YWFhNGvbhJDX/Vn87VbyEsCYpgVs17OTs018+H/fUOJy0boy2iu/yciyjKerJ3cNuwuGgcls4svFX1KYWwjJ8MVHX1xyTAIJ7GEPUrDEs3c9i7uhDoqRNcD3xqCmnjz65WBy00vwCnBGkiSCW3jy82s7AcgodmXYiOpfGqgN39atGfHSSxSlphLz1Ve41PH0ZledDEZoL8Vzds7X+GrjiFbPEg3s9mzHV01u4au9yTzRrD/3x/7Kh0++wunMUnYGdiPTyfbNX6taaaKYudPFmesjQ2gf6F/+7b40L5Mt8cX0CoSAf1wuqQnVYuLQugUYD/5B26JtdKGMfNkT97RC9JKFQtUZd6mUxI8XkKL3pZPpIBtD7qbVmGkEhkTY/o1rHUX1PdF7IgatgQXHlrG/YBH/tzacd0bcW/68xWJh5dYtHF6TiltmIFZ9GRMmXNrbWlWqolLHK3kIQq2IhMWBjEYjc+fOpaDgwsyTKVOmoNPp0HnruOMZWyn7I3vj2PC17RKEikrbqFBc3SMoMZXi7OJC867R1Tqvk86JJyfaSoIfTjjMwg0LKUgpQPVVmTBgAs0Cm5FVlMXyXcspPlzMN8u+4akJT5Ufr6oqq/avIuZEDHonPWP7jiUqMKrar78B5ivlvANdLrrvStNO/pw+kEmB7MPK535h1IfXbirteXpXVzQWC4ePHaPX1XevsQmdgnh7cxZ/Or0KgEnqijr2cw7EzOPDE+8yvmAPLza5h8X+A3kp/muMOSeIspbi4dScrm29cFv5O/7HDtDl9b2Vtp+38yfM6GnTpnmlz1fVmZMHSD+0Fv9j8+hoPUOcphknm95FxLBH8A2JxGJVKCkz4WYwcGzXakrWf4izJZ+d3T5kwOh76sXlkQe6j+aB7qPpNmccezM3AbaEZcPOXWxbGItnQRCKF7iPLOS2Idfh6+5d43OJS0JCfScSFgfas2cPBQUF3Hnnnfzwww8AaCvp8m3btRmFuSUc3nSWPuNa0qbrpQvw1VS7yHa0m9ruku3BPsG0D2/Py0deJu1YGjuO76CgpABfD1/+WvMXcprtq5iCwrxD8wjuFsxDY6p+GUJS5XoyR8g+Rj3UnqLcUn6evpHEQl8yth0ioE/dLWxXGb2HB8FAmiShWCzIl7l8UFsbT2TgJltJNrQixHgCy90f4xrSlk7t+7BlQ1e67HiL9WmbeTL8ZUaZ3qJvJxfY9Tud4g7gXOaK35FYil0uP3bKENIO2EJWwjHCr6t+fKqisOOXN+l96n1CVYljhk7EjPiE9v8YCK3VyGjPzeaJ7jkceg6v/smuEW99MGnWrdz027MocRaGJNyAxktDu7s96N99oF0G0tt6WETCItRfImFxEIvFwpo1awD4448/yrerauXfcnoNbU+voVVYbdXO+o7oy64Vu1gxf0X5NhkZl9YuPHPLM6TlpfG/H/7Hmf1nUK5XqvzGKSNjOXxtKrNeK27ezkx+ozfzXt3Ljh/3ccM1TlgA+vTrz4Id24n9eyktbxxn17Y3HD7DJyti2J8lAzKFIz9FWTQK1zm2woRaJAagkos7WXjzQMpHTPKZSG+n/rjrfMnVNSWzpAnxPV8j+thXfP7ENIpcnXFTVYyyDFYFnapg0mjAyQmppKzaMaadOUXGTw/Q27SPnQG30HrSW7TzrrsByNfKB0Ne4eHVz3K8aA398u+jUGPk2ZkTK/2CUxOqqqKqiFlCQr0mEhYHufiNprjYtpbQjBkzHBTN5V3f63p6tu3JN398g2SQsFqstI5szbje45BlmVCfUNq0a0Pc5jgW71zMoI6D8HTxrFLbmiaVl7lvyFz8bWM08os1KGVmZP3VZ3fYU8uhQ5C3bmHHrp00v2GsXaewT/3RNsXbhTK89AohPq5oz009Lg0djvPZVQB4U8i33EaW5x/oXTbhc3I01ze5D4C0cIldB0o4Fv0gVstxWrjlUlRURLCsQXZ2pshoxOCkx3jiBJ77TsPDVY/v+OpvCdz6KkHoODLwG3oOrF4dkvqsQ0gTtt41n+IyI/NmH4HcfGbNieGJBzvXqL1ZDzxJaX7iudUOzycpEnkZ04CqrZ4uCNeaSFgc6Hr/PizP2F6+anHKySRCWtbVrJya83X35YW7Xrjs88M6D+PYjmMcWHmAndt38t+n/nvVa+GKZEX2Nts7VIeTJInh17uxalkoMXNW0vHhui+XbzWb2fHddxw/fZoWTZsSparE6fVkHjlCYHv79crd29mDOfsLmDG6Bbf0b0dxoa3uS3zYFKKSvgdgiTKSWDmUQtz4290VCRMddYWU4oqz2cCuwm2Mfe4Wtr23lUJzCDe88CBa7aXVec+M6ERZbtX+PtLiDpK6+A06569hp8sAmk3+hLah9rtsWp+46g088GAn3ntlPbqDOeQWmPD2qH5FXGNhKq4+zQht1R5VUbFazZzesxgXt5I6iFoQ7EMkLA7U5db+BL1vwG1CFGl/HCX/UGq9TFiuJsgniFeee4VvlnxDTkwO09+azrSHpxHkXbPplQ1dixt6sHvJfE4esNDx6rvXiqqq/DBzJgmShIfJxLrkZNBq8TAa8W7WzK7nenFCL1YdW8JbK09xS/92yFrbB2VU0vcoqisFlvEE625jn7oOF0mPv9kbnHoxu3UIXfMUtAo4FRq4fttYBrQfQpsjN3DwyxV0fWz0Jedyad8M45pjV4xn/4Y/sGz/io7G3UiSF+ubv0j/256vNAFqTNZu2YtTwsfIuhZ899gmxj8/lYh2FQe9x+4+SuLh4xdtqfgFQlXNBES1Yuy0qQBYLRY+nrwYnZP4SBDqL/HX6UAaLydkJJwkHZJGpuxEHoe/34pvh1CCO0U6OrxqcdG78PiEx3kt8zUMaQa+WvQVr93zmqPDchg3nZEkJZyd7y6k5/MT7d7+qdWrSY2NIy0jgwRJYnhgIL1fe430QzFkxp4iqm9f9C4uV2+oGnQ6HS39nNiZYuv5OL33IG3PPRcX/AHOCU0IssJk+qNaVW6P7Ue3Ee4cBVafu8owdLcMqsohXQxtuIGtR4xUtoiBompBVjDmJGPwCoHzl7YUhRM7l5G7aTY9SjaRoGtGTNvnaDX6cQa5ulXSkmN9/tGX5CYncN1Nt9K/d80u3/xTzOaNyIBiOQOqid//s5fB90yn84i+pMYmsWPRSk7v+fOq7XgHVW0VeEGoL0TC4iBpsSc5vX8P4S6RpC85gcGqw71QA0cVMoyJDS5hAdvlkDceeoMPFnxA4dFCDsUfokPUtR94Wh8MfXUs699dyd44fwKWbCdqbO3riYCtR2XL57NYl5GOeu5D3NlkosfUqUiSRFDHDgR1rLvfeUmZgpts5c9nZhK5cj5po9vjpO2Dc0ITrIoFjazFgI6zxlMEOkfyiFnPLJ1t8OzAmBJu2pTGy7FWir7/L9+mLiffkM5jpjHIThUvaySmmPCWrBg+bUs+bhzqMhNr/llan55HKzWTJDmUI51eod24p2gq148eFVVV2bRlD6lJSYCEwcMD446/cQbWfZNJ/95zatRucUkprudmVe08cAL5yEasnYbz/PQn2Prbanb8/gnrvn2LMuMzJByMIfnIKiTJiejrJjJ46gTbGkEXggRAkmScXGq2uKIgOIpIWBzk5K5t7P7rd5J8OxDq1RU/2QOdoqFYY6L1Hf0cHV6teHl6UUghS7cu/dcmLC5Bvox85xZ+eWQhGxflEj6yOxpd7f+7bZ/1BWuzMmmj1TL+2WeRVRWryVQ+PbfO5RUwfc08mued5fTwmwi5dwqrZrxADrMwKyZ6tW1PXE4R+dlpjAl/iNADidDdVnYt101TfmHCWy4i12clBZIJ6R9l5zMSU3A+cIpDzbuQ1u16PI//Sv990wAoVJ3ZM/gnuvS7njCN46qcWa0K773/JYVJcchmE04l2Zh1rngUp1W6v0tk1Wslbd1xgNULFiA7u1FWkINnxgma3HgvBhdnDq5ajkHWMW3a/QD0vXkYTaIj+f0/T7Hl5w8A0Oi8mfbjD9V8RY2pyIDQWImE5RozFheRlZiAu69tqmVy9iGCxval9ahhWM1WZK3coIs3xWbEcmb7GUwaE5MGTnJ0OA6l0WnpNsiftZsgIyaJ4C7VL673TwfOJKLR67n5tdfK/040dVzZ9mITj68hpCQH3ZffMnagrTzdrT8vJPvwIf569QU2H96LFgkLKj8pyykqdcJQNhKj3gnPYgXp3Dd8zdwMdG1D8Td4I100k+nv1fvwefZBvFWFLv+dSWT7FjB6Cod+fZ3oY59ytNm99Lyu7gcy/5OqqsTGJzP3u99xsRRjSU/EvTAVjWcYGlRkVy9w8SF46GgGjxiCjEpRYRHxZ1I48PnryIc38OoDcbiFRmJw9yQ3MwPJVILOyx/f4GAsZWVkxh5DLS3GNTcRndYFWbWAwRMZlZQ/vwFArzEQOf5eDBclqBHtWjDkvlfISjqLhERIq8Y54FgQapWwvP3220yfPp0nn3ySjz/+GLBVb33mmWeYP38+JpOJESNGMGvWLAIDL3+9dOrUqXz33XcVto0YMYIVK1Zc5oiG6cCqZWz8YQ6WsorTeXd/9yUHF/+OZ2gYwc1bcd248Tg523f8wbUS7hOO0dmIzqRj2bZlBI4OxMvVy9FhOYxbgAdQQOHZ7FonLMdWrCDDYCDUaLymSa2iKGQfPIRbcBCRCUexdulAy4EVa+n6tutA1xFj2bl2OYXnZr2Z9aBRVabu3kCnyOsJKC4ioEsY+V1vx80UgqduLEf1FS/nnE3KpJmpiCwXb/q3b2HbKEl0uG0GMIOe1+D1Xiy3sIQ5H39O2bEdOFlN+ALZOm/0Ht40ufUxnplw+bWOAnw9aRoZSvNmc/h74RKKYo9RGncYi7kEDB5Y9M5IaacpPFKCCiguAeDigXu/cUy59w5cXGwLKprLzOzffxifAH+ahAZiqGSqfKdhdVnbWBDqhxonLLt37+arr76iQ4eKXf5PPfUUS5cu5bfffsPT05PHHnuMCRMmsHXr1iu2N3LkSObOnVv+2Mmp4V1ftZjNpJ46zpZfvic/M51WvfrReeRYvIKCSYs7xdo5s2g/eDidRti+Ibp4eJKZlsredatJjztF1omjZMXs4/CKv2g1eCQDx9+Eq3v1V111JL1WzyP3PMLsX2eTczSHt2LfYuqUqUQ3qd7yAY2Ff7fW8NsOji07Qsux3ap9vKqq5GVlUZqayvpNm9ADd7443f6BVmJft544lRRT4u2FW3Y2WYAroBtTeQ9HxwcfofnEm/ny0bsBGDZ4EFs27yPcI4Qb72gPtAeGoaoqZ6dvIcDQioXBerbv2kvvHrahtw/eM4IlRyYRtuJ3stJz8Aus/eKCiqLw7Q9/opQZGTlyIOFhl9YZURUFi1Xh72XrKMovwKKo+Pt6cWT9SrRnj2ON6kZw1x4M6NuNJiEB1Tp/RGggjz5x32WfN5stWJEw6Cofi6PT6+jR0z4Ddq+mIffuCo1fjRKWoqIiJk+ezNdff83MmTPLt+fn5zNnzhx+/vlnBg+2lcGeO3cu0dHR7Nixg169Lv8twMnJiaCghjsNdvvCX9i24CcA/MMjad2nP4fXr2H/ir8Z/tATpJ8+hcHdgyH3PoLmoqJxbj6+RLW5UBr/6N7drJv7FceX/sGx5X8he3ih0enwDmnCpGdfQqfXX/PXVl0R/hG8+dibxCTE8NPPPzH3+7mMvXEsXi5ehPqE4ul6vrBc439zdPJyw9+aQola9QRcURQO/fQTJ+LiyCopIfP8bB+9njZOBgzeXnUT7DmrDiTwwPwjLC+yrXHllp2N8YEH0Bic8GzdmojBgys9Ljsti0VP3Ff+z3p039tI7q0ICvzHYpDnhkv0DPTl79wEvkkpLE9YAFxCgnGymjm0fCODp46v9euZ98NC8pfZenDnr5lPcVBrhtx6G/17d0ZRVD778mdKtv6Jk9VY4bgcbG+QhX7NmfH2jFrHcTk6nZZrW15QEBqmGiUsjz76KKNHj2bo0KEVEpa9e/diNpsZOnRo+bbWrVsTHh7O9u3br5iwbNiwgYCAALy9vRk8eDAzZ87E19e30n1NJhMm04XLKhcvHugo55MVgOgBg3Hz8aX3Tbez6stPWfnFxwB0GzuhQrJSmTZdu9Oma3fOnDzB1r8WUpiZhrm0lMyDe/jm5WcZ++hTNIms/ViIa6F9ZHvuu+s+5nw/h7UL1lZ4TvLTE2iKdExg15CiKGRqmwCQtvMYQT2v3tO08fPP2ZidjXtpKd46HSODg/EKDcUjMIiQ7tXvpamus/m2D+47h7/CQzF/0ivtKPm//kX/betIT0ylKCuH1NPJlBYUYdU7kbBkJe7bNxCYfZbithFwbjCsq0su4a1X4mv158zuLACW5nXC7Ug+gwBZIzNKMvO9XzCZmVn4+9vGdVn/XEhSYBT9b7785Zaq2r77IGdXLUQKas19r7zMwvl/YNy1jt2fvMrmz12waA24GXNQwjvh3Dya6BYRdOvVFa0sc+T4aTIzsxjYv0et46jvinKyHR2CIFxVtROW+fPns2/fPnbv3n3Jc2lpaej1ery8vCpsDwwMJC2t8tHzYLscNGHCBKKiooiLi+Oll15i1KhRbN++HY3m0m7St956i9dff726odepp37+i2NbNnBkwxq2/voDVrOZyE5dGTvtBToOH01pYQHNulb9jS+8ZSvCn3sJsF0WmP/hO5zds435LzyO3i+Q0Y9Mo1nba7+2UHU1D23Os48+y9IdS/H18SWjIIPCokJy9uXgYm383ytlWWbE9S6sXZLDuq/3c3sVEhavwCDIzmZoz550nDDhGkRZ0d3XtaZHhBejv9zLzJ5TuVdK5KZFn3Gyra3qSv65/XTnflogkR7clMTRk9AE+WLZvRSjkxPDJq0kZc8npLEAtdA2tbnL8ccITevGsUA94c28eUjfme8PJXL/qq38Mm4Izm5u6I3F5Ldoj7Nr5TOfjGVmjp6I5/iRE5QU5OEXHMzIoX3LB6KmZ+Twx/yFZMefRJd6EouLH3dOe5Igf28effxeCgpvZdWKDRw/fgprXhbtRt7PiGH9LzlPt07/nsuY8fttK2f7hkU4OBJBuLxqJSxJSUk8+eSTrF69GoPBYLcgbrvttvL77du3p0OHDjRr1owNGzYwZMiQS/afPn06Tz/9dPnjgoICwsIcWyFW1mhoe90Q2l5nizfx0AH+en8mf743k+EPPkF4u5pP75UkiUnPvEhBTjZbFi/i+IZVLHrrNcY++wqtOnW5egMO5uvhy5ThUypse33Pf3DTNa7FDy8nqG0oHovjUKTL1wuxGI2s/eILkrOySFdVDKpK1BV6JOuap+uFS1hz1AiiO12HQVJRXN2QmjbH4OWBX7NwlFIj0f270s73wliTL+bF88PRDqybvYy59z5CK4+XsRQXsmvHWFT/LahnuxA0JIJWkbZjPotL5D6nCGYuXs2bt4/H6OWH86mDLF61hbJSI22bh9G0eRRHjp9m2YLf0Zzei16xFa9TkMhH5cNfvsQS2hoNCtbUePSWUko8QvHvPITHH78fl4tWh/Zwd+Omm6/9TKP6zGqxoHVyIjDKvtWRBcGeJFVVqzwB/88//2T8+PEVej2sViuSJCHLMitXrmTo0KHk5uZW6GWJiIhg2rRpPPXUU1UOzN/fn5kzZ/Lggw9edd//Z+8sw6M4uwZ8z8z6xt2IEIIGd4o7FAoUKtSpUHd760LdaPvVXSnUoLgXd/fgcfdkfWe+HxsCaQIkISEJ7H0RsjvzyJnN7MyZ8xwpKirC29ubwsJCvLwaj5Pqyd07+OuNFzF4+zBl+hfo6igTZ1ZqCr88/wSyuZSI7n248o578PTxrZOxLxavvDiN9nFdufrm0Q0tSr2x/8fl7FidRbHKtdQxZKieVtdWfpKXnU5++9//OGI0ElRURDM/f3pePYGgtm0vtsgVUBSFuet288zCkwiCwLsT4xndtXo3tE0JW7jvt6MoiHxxQ2t6tuxEys6/SMh/iuITV3A8ZxiWVn60jwggSE5hz+4F/BU0jCf9m1OyYBFHj2+rclyLpEfX/gradu5Ehw5tCQkOYOvuBJbPnoMj4yQOQUIyenLzgw/QonnTK3PRUGyd+xeb/p7Fgz/83tCiuLnMqMn9u0YWliFDhrB3794K26ZMmULr1q15+umnadasGWq1mhUrVjBxoisdeUJCAklJSfTuXf1MnykpKeTm5hIaGloT8Rod0R27cNcn3/Hjk/ez5pfvGH73Q3UyblB4BPd8/DV//t/7pGzdwJdbN6APDqftgMG069mHgJBQxCqW0hobl3qqqk1rS1Aj0b2zQMuxXfCOqNona837H3DEaGRYdDRX3HbbxRXyHAiCwLh+nWgTFcKd367jwT8OEOypoWs16l31atWDOfeHMOW7Fdz8wwleHJWOR24Knl5g9UyiND0d565jbN8FdzGDnmRyV+pf/OA3hg15kWj0Udx167V4RsWweXcCBemphEU2Y8ig3ngYK4b89+rShl5dLp/lm/pAdjoRz+Nf58ZNQ1OjVJGenp7Ex8dX+DEajfj7+xMfH4+3tzd33HEHjz32GP/++y/bt29nypQp9O7du4LDbevWrZk9ezbgijh68skn2bRpEydPnmTFihWMGzeOFi1aMGLEiLo92gbAKzCIfjdMYe/Kpfz5+guc2LkNp6NiFdoaGLnKMXh6csuzL3P9Gx8S028I1pIidsz6iZ8fu4cP75jMHx+9S0ZSYl0dhpsaUpySjU3QERMl0P3eYWdVVhRZZnNuDmFZWY1KWTmTlpEh/PPEaDwlJ4/O2IrT6axWv8jASOY+fA19InN4foHMyjwtVpsGf/9khgyW8RFdlYGz+j2J47lskrRh3JY3H+8AB/NDRmP1C6N580gmTxjGvffdxrgxQyopK02R0pwCsvYfRZblhhalHNnprNJf0I2bxkSdq9TTp09HFEUmTpxYIXHcmSQkJFBY6HLdkySJPXv28OOPP1JQUEBYWBjDhw9n2rRpTTIXS1V0Gj4ao68vG37/lb/fehmNXo9PSBgGbx9MBQUoKNz4+gfnjSCqiojmsUTc/wgAiYcOkrBrO/uWLyJpw2p+2byO2IEjuPK2O9E0gXDoS4X0zQms+nYHkuxB+xv6VNqfvG0b6+bPJ89mI0BWMOv1DOnQuEsY+HoaeH5UHE/MP8n0v9fxxDUDztunwGrHqDbw3dQ7eH/+L3y+MYyjuXdyX4efESxf4xvdBo63oE23+0hK2Im3tZQcMYCnHniQpdM38dDMHfwd7ENYSOBFOMLaoSgK+QeSQC2g9fVA5+tV6XvssNs5/NFSxGwFlaBGo2gRBYnDjoPIrVXE3TAIteHsPoHWohLSV+9HLrWDrKAooKCgaBQUFIqyssglHafiQHHKyLITRXYiyzKKrKAoMooslz8YCYIAAtgtFvJSU5CdTjz8A3A6HOxfvcLVTlFQlNN9S80WzGodHn5VK97//UxO/T7z58xtkiTRrVu3S+Ya7+biUCMflsZKY/Vh+S+KopCdeIITu7ZTlJ2JqbCQtMMHMRUWMODmO+g25sJzToArlDYrLZV5n06n6PhhJN8Axj38FDFtGtYn4kxeeXEa8XFdmXiJ+bDs/HwhG3brQJEZMVxLi4kVfVasxcV8PG0apQYDEaWlpOr1RCoKt7z8cpN4wh315lwOFwosf7gPMaEVb15PrUxgw7EcFAWys0qxldhBElDpJERRwFbssiwafe183HoWiscWfAsVvKxXEpfwE0elWILunYdXQDhrNuxg6pyTGEUHc58cSbh/w32vnXYHact3U7gvFXW2hAo1Jo9SBIOENkeNntO+aSXOAqwhdjxbhCKqJFR6Lflrj+NrDqDQkIdVsKD1MaKP8KN4ayr+cggmuRihuxHFLoNNxppbgi7Wl7jx/UlZuwfHvBxUohpZkV2KiiIjChKicNpAvr9gA/vy1577QE4lhTtDcdF5eOKw27FbzGfvJoqYA8Ox+wWjquZD1akEdIIgVPg5hdls5rrrrqNNG/dS3uVOTe7fboWlEfD+da6Ihcdnza/zsbetWMrqH79GsVuJ6NGXax56vFHcGF958TXi47pccgrLiYVbWTi3GIDeHax0uW8U4FJWBUFgycf/x8a8XKYMHEjUwIHYTCZUWm2T8DkC2Hs8jbFf7eSalhrevX1YhX2t3l6OrdSO3kODj7+enlG+5JvsZJdYMNtknChgM5PkIRAZXsp3IdmkJL6Oxman854irPefwNvbp3y87bsOcMuv+wnWOJj77Lg6WQ5yOhw4zVY0nueOUJNlJ6IoUZyURfJXm/By+GJxlmL2MyM4RXTFWlDA5GlCFW1E7+uNKS0P04FsgrSRSMLpG7usOLF2UoibPKjSPBlbD5H95358BVf2XKfiREFGJagpJBeD7IlVNBNwc1uMUYEVb/xWBVupmcJPDmF2lKC9OpiInh1rdS6Zi4uQNJqya4OAIAoICOV1nv7vzdcwWW08/fKrNR670lxmM2+//TbXXnstbRvYsdxNw1NvTrdu6pedS+ZjLipCdjoIbt6C8FZtMZxxAa8N3YYMp1XXHvz04lOkblpDzqTrCHbnWqg3YkZ355ZOecx8YS2Ht2bhv2IFxw8eZGtKChaNBlkUCSwtJXKAa0lFY2haPhntm4cxPHI3fxy20WLBZu6+8nR1H1GAyChvVt9ReRnsTNan7+KWQ2qmZirM7DyTXbuu5/d2t3PLf871rp3a8mZOEY8sy+L9X5fz0tSraiSrw2YnY91+UEPJ/iyUkxY8OR1N58RBibEYdaABbYw3gZ1boPHQc2jGSryPeFBILj5CAAbZA3NfiBk1BJX6/LmDHFYblrwiHFY7thITGk89PlGVywEAhHRvTUDHWPL3JuLVIhSVUYsgipz4cz3KASjRFRNzWz88QqpYijGAxVHK0aIdRHm0Q5xbyr6V/9Dh+Zrn7tGfpQTIF+++RUGJCZssoxXrNjP1JfCs7OYi41ZYGgEP/fgnf0x7jpXffYFKo0VrNLJ5tiu8sN3AoYy895Eaj2kryGbXT28Q23ck/p2HE9yyFYmZaWgbU662S/SC5Rnmh1PjJFXrwfdrXWZ6g9OJ0WajZ0wMvW66qUnXbPninhFc/d583lmbhSJs5Z7R3QGX+V+Wz/83vSK0E9/K27j1cCC3HUzkTtmA8ywn5lVDe/HGst84nn3uMUsyczn6fysIcIRS6FMAJU4Mdg/UgstHwgM9FqeCSSjCGuRAlSNiV1lRF2tQFStIJy3k/bsPAB88kQUnkiJRqMoj6v6+eISe33fjFCqtBo/QgOq316gJ7NqiwrbYKsLfq8I7MJiBnz1M7r6TmH9JRlfHtccySswgiPjp1bSIbXH+DtWgKZ/7bhoWt8LSCFDrdFz78ltkHE0gJLYlklpN5vGjHN60jq1z/8JusdBj3CSCm1fvguG0mvjugVsotUusXX8ElfAhDkWiuUcuPl90IE8VhAM1Do031hajCRp8D0afMsdGRSFz9nMohxZijhpM0PBHMAZG1uPRX5pI/lp0pRLjRo0iMCIC7/DwhhapzhBFkRkPjWTyR4t4e00mKmk7d47o6lJYqqmDDgzvRquE9Wx3xhKpHksPq/WsbUP1kFJSOTJJlmWO/7Ue28ECvEw+BOBKg+Bd4EOBKoeioCL8O8cg6XWYM/MJ798LvZ93pXFspRayth+iYHcKKrUG704RhPds06RurPmHk9EBoRPq1nnbICjo1CoeevaFOh0X3BYWNzXHrbA0ElRqNRFtThdBDImNIyQ2joBmUSz69AMOb1rH5GnvEday9XnHclqtlNolurY0ENKuG1knT6AxaPD3iiEztRgkNQ6ND5qSFML2vAd73uNE/CP4dZ9E4eLXiUxf5BroyLdw5FusTySi9fApH19RFEw5yW5F5hzYzAqSRkWLnj3P37iRkndkK5Ik4d28cjZlg07Dn4+PYeRb83n933SiAo8giFTLwgKQXWxhj+DyI1EX2WnTqrJ/xylCPSROmCQsuUUUHEsld90xlHw7kk2Fp+CDDh8KhBy8B0dhaOaPf+soImpwnBqjjoj+nYjo36kGvRoXluMFKLKa8GZ1G1HlkBXy7E5efvllBnbrwsAxNVuWq4qmpAi6aVy4FZZGjCLLtOjRGz51vQ+sZtFDjZcvoT6QllnMgJcfp/XZnPBkmcw/Hsfz0Exi9n0I+z7EG0iLuJLQO34l7a/nCN/3KakzH6f5nd+Wdzv+ek9iHQnYUJPpEY8iO7Chhui+tLh2WvWPr9otmw5F+SXM+WIDlBpQBdgaWpzaI8v4/eoqYpru2QGhx1SC+96E3VSIqNKg0hpQqyT+emQ4Q95axF2/H0bQSRj11Vtz9DdqEUwOFIOK10e/gadGxbE968k/tgOr04k6bSuqPg/QuWsvJub48ayoJefd3QAYZB0lGht2rY18fS6xdw0iIqCy5eRyQrE4kAWxzpWBHp07smn7ThwqNdmZGaQlnSQsMrpOxnZbWNzUFLfC0oj54p5bMBUWlL9Xa6qfs6DP1dfy13e/c+inl2gz5bWqG4kiwddNR1E+IHnpp5C4AX38aML63ARA+KQ3KDr4G81T/iT/+IMYAiJIWfAesY4EAI5798HblgmSmgDzUQwHdnDyl2KibvigPLrgcmP2F+spTlQR3lXiypsr18Fq7JRkHKPol1sJKzmd0Tq0eA+seADHiofR4MSOiqTI8UgthxDe9wZCQ4sYn7aNzap2tGwxsFrznCg2o00uZKR6G0sLt+GXfZhBqV9WbDR/PlvntqeDeB1WuRMAuYYsWtw3mJiAplWKot7RiEiKiuL0bDxD687KMnTCJPqNupI333iT/clpHP76G56bdpbrSTVxW1jc1Ba3wtKI6TTiSjb8/isArfr0r1Hf6BG34PHzDPZv3UGbKeduKwgCzUY8ADxQaZ9tzCfwzw3w60S0zhxigQK80T6wjtYBp5eEFNnJ8e+n0vzo9+S8voj8sAE0v+UTJPXlk7DO6XRSnCKg9rMy4a6mo6zIDjvm/HREjQG+7E+YUgLAybCxRE/9hZLMk+QseQ/MeQjWQkRzPpFJf0LSn5SseIp/lFIktQwOyNkaQNp2DZ6OYvar4nhCvgenfziyUyE3z4zD6sTgocZicTDSupEvNB9CMtgUFcelKHQ3/44gSQQGBHFs1U+Ebv2KQPF58myPYpKHIOnVGN3KSiXCxnTE/FMyJz9bS/tpdVvhW6vTc9WwocxdsRK7pMJms6KpwcPT2XBbWNzUFLfC0ojpMe4aJJWatTN+ILJdzZ3pdBqRklIbitOBINXuTx3Q+Uoy5RkEz7sBgDRjO7zvnI3eN7hCO0GUaH7Ht2RuHo2y6i3ikmdx/NMUoh74B0nVmEKT6odlf2zh8IYcBKeO5h3r94ZqM5eQsWUO5oNLCc1ajVnyROn7GCEDbq/xWE6bhbSPhtGsdA/gqn58LO4ugoc/THSgq2aQR3A0Hrd8Ut7HXJRH+u6FmEsKEA4vxmYIoaj5CLqvvZMAW055u16OHUT4OdlbZMVql/H30xPkpSOpwITRQ8PoHlfDgg/Z3uJB2k96juY6fQXZWo1+EGXEvVi/vAO/rOkYOwYTMeHe2nxklzy2IleZA1V4/YTJd+nXH7VGzV+LlrB55Qr6jby08ie5aRq4FZZGjKRS0WPcJDKOHmbznD9o3XcAGp0eRVEoLS0hLSuLDIudXalp+Oi09G3blub+rptl6qpZ5JSKaEQ1iqwgXEBesuCuV5Ij/Eppwr+EXPUiWuPZ/QWCe06EHleTOucVYnZPJ/HP54m+/u3aT94EyEjOIWFFMegUuk0KpNfQ9nU3uCxTkHyAvMVvoS5JA4eFcPMBIs/wAJKR8Pn3UUpCW+DRsvqWuLR1M/Bd/hjNMJPu2Qk5vCvGThOIbX3ukFq9lx+5IR1wzJxCS+dRyAdS/wDguCoWW5/H0KgkvHwDmdl+6FnHKSwuhgUg+TRD8x9l5RSCpEJzz/dY/8+Jdvf/sEd3RN353HleLkeMYX4UKNk4c6zINgeCWkIQhPKEhXVBu+49+Wf+AtZv3EjL9h0JrmXk2yl53BYWNzXFrbA0AfrdcCs/PH4fS7/8P3xGjOPehFRyPHxQTvmJqPzAAexJJKZoO48GaXnMFkP3boN5zHEQsRrJrs5HQJcxBHQZU73GgkD4hJdJStqCf8IMZMc0RNWltzSUfDyDhd/swJGnQ0Bg8nNX4BfoU6ux8hPWo5l1HYXqYOyhXVEFxCIenENA6WF8cGBATY4uBkEQSYqejGfnCfjGD8ZSXEDJwTX4LJmCasYkMtXBWFpNIPTKp9HoK2dzNeWlk/ftNaicVoIsx8jSNid/yIuEdhtTbb+jjKQjeP86En/FzoFe72D0DUatM6DWGYlu0QOxmhY1g8GIVVEhWwrP2kax2XAkHweNp+t9SX61xr7c8IoMIbnZdnxS/Eh7cSN22YpVMKPDiFlnwntAJP5dYlF76hGl2vmXiaLIyGFDWLhsBV99+QX33nsvAaFVJ8Rz46Y+cCssTQDf0HBG3v8YSz6bzhx9CNbQKB5TWwj18sJbJRKuUSNIIuuSUvlQrechixHUEN+pFf2GPdZgckudJ+O5cj25b3XA//lDVbRo2k9Yy2fswpGnQ+VjRe8r4FvLSBXZbsX3N5eJ3WQ3EHZyFpyEHCmYlOhrEEI7EtBxOGEhsZX6GnwCUFp0I219BxwqAypbEVH7PqJw3/ekNB+PR6dxBHVwWTkUawn5P95ERKnLofZE8GjCbvkSrbFmycYSF31IT0rZGDiR3qPurtUxA6glkTz0YC2qcr9stuB8fyBqx0EALH4T0fW7stbzXeq0vnsEx//ZgKPEgqPYAkVOHNoSxEwFeWkh2Ut3YJXN2AQrUhsDcbcOrrH1pVv/QQSGhPL9jJl88sWXvPTyyzUew21hcVNb3ApLE6GwXVfeuvNlAOKyU3hybGVrR+fYWK4vLeWllfP526MVPirTRZayIiG9roGVD+DvSCf/yBZ843qcsbfpRwqU5NgRtE7ufqv2N1FFlkn+6nqigMNtHqblda9SmnkcpwwBoc2pTr5UY2AkxidOF74rPLyeovkv0vz4T3D8JzLnhmNV+xFu3k84MmnqGAzXf0NMbLdayaxSbBwTIun9wHe16n8mJYIB4SwKi23ep2jtCVi7v4MYEoe2Uz+SU4v4ffFhIgKMXDfOXTjvTCS1irhJlZcEnU4HaWv3UpKcgy2xCKEUPA/5sH/6POIfq3lelahTuaAEAXNJMYY6zq7rxs3ZcCssjRybLDNkawJHTKczgX7cp/NZ2wcajXw29jpsi/9ghuDL44pyukrrRUbS6Ejp+w4R657COetWeP5gg8hRXzicdtQYcDicqFQ1dxJSFIXCt+OJsqaS2OlJWo5/HgBjcPMLksu75RV4P7aC4vRjlMy8CxQZ75ITpAYOQOxwDWF9rr+wYouWQsLkDFdphQs8t0yCEdFaXPXOglQUtAihrSkKbE3K3hwmz9qBCZCOwLgRLdDpLn2H7gtFklQ0G1jxmnHwf3PxyfLlxNyNxFzVu9Zjr166mFETr61RH7eFxU1tcSssjZxDpZZyZeX9Vs24Max6NU0G+noy3xTAg4t+5YPhk1HX4oZaF/i0GwLrwK6q7E/R1K9XXn5GLJlqzCYLnl4Vj0+2W0hcOwvzvvl4mlOR201A9I7AdGApOlsu3iOeIWXWE8Q7UzELBqLKlJW6xDM0Fs9HV5a/r6vUaha7jF6wgSJzQd7cgFk0orZXrbCor3qUws8X887fc/nN6Qq1DhclWmtV7DBbm/z505B4Dm9GybJUPDZ4s+/AP7R5/Eokdc1vB5t37amxwtKQ5M+cRcbLLyPodCCKKHY7HldcQdi777h8uFQq129JumxzSTVm3ApLI6fUKQPwWdsorg6ufrjsjT1GYF8/hxfk1uQsm82T4d50iR8CF/lLqDa6ZA61HKliWajpYrPZKM1xIKFGEl037fwjWyjY9DOG1PUEW44RA5gEA4qiYNz2n2RbM1bjU/ZSvm/zxRT9glFp9OSW+uAv1kxZKTTZKTDbiPI/rdyZRSMGe0mV7RftO8JL9hexyhL3haTQMm4gGp2KvzYlIQKFpTb01cys66Yiwb3a4t22GTkf7ManwI+T8zcTO+GKavXNTkstf61xOmo89ynLysVKIOfIz8d24gRFCxaS/6srr1XQ44+DIpPzxZeUrFrF4e5nvy4FPvII/ndPdSe8awS4FZZGTojGdUH+MyOvRgqLIAhM6TuByN0ruTGvBf/mwryjO+jesnZ+C7VF6+XPyYjxRKfMoWDVJ/jG/XRR568v1Go1KCKKsQSdTiJl/tuEbnsLX2QKRV+ON78ZddwQInqMRQAs+WnkJR9CsRSj9wlCmfcIBUG9aH7L/2FsQk9ye9fOo0veAnaHXUt16xcrisJHK47w0fIjKIAogLdejbdezVBTLKPEo5X6LFw4hwfWqBnlkcnLNwwmXw7n4R+3kWC3A3BThD8hAZWtdm7Oz9E/16LbBrIioxY1FGhzaTlkWLX76z08yl8/+fKrNZ7/lMIiXqTzPv1/z1CyenX5++bz56Ft4Sok633VVZRu2QJOJ4pTBtmJ4nC6fjtlMl56iewPP8Syfx9+t96KLj4eUae7KHK7qYxbYWnkhJet0WvE2mn3QzoOptei2WzSxdA2Iq4uRas2Ebd+jeP1eUg+zf6zp+na9AVBoHvQUkJs28l5+1ki7MmkenYiYOrfeHv6V1p+0QVEEnZGZmDabK/2Db+xcPzAFqJW3M1hXQc63/7xedsrisKPG07y3tLDlFgd+BjUXNUxjL0phaQWmEnON/ONfCWritNZ/p++C3an0kmn47NnHqDU4mTkayvwFAR+HNmWmFYBNAvxqHJON+dHpdcANkRBJD88n/gHxtXIerBmyWIAwg0a1Jqapyu42BYWxW7HeMUVBD/3LCo/PyQfn/J9ko8PXsOHn7WvR/9+HB00mOJlyylethxNbCwBd08FQQRRQNQb8BjQH+FCfMLcVBu3wtKI2VFUypS9JwCwy7Uf5yY/FZtMcOfq5fw2amIdSVd9ilKP4IcTSX86muBSMK725jvQgNMucaLHa0SPuLfWGYUbO7/NX8LkbddyTIwh4p6/zltyYd6uNJ7/Zy+FZgc6tcj9g2J5bGhLpDNygNgdMi9+/w+/HQvl4O5NtOnYq3xfmkVNM08FQZI4ciKHfEVmSqtQBgysXgFQN2cn+sqe7N76B/6WENRqLbLsRDrPeZt45DARzWOZ8/OP7D2ZhK8Edzz+dK3mv1gKi/X4CUrXraV0wwY8Bg9G27zmzuzq0FBabtuGMy+X5LumYjt2jLSnKh531C8/Y+h2cS3XlyuX5tX1EiDNYmP09iMAvNUyghtC/Wo91qSeY9mx7He+07XkySUzmRAZyQ8nkrg2JpqhbXqdf4ALRHBaAAjf9gYnMg6iazcacNb7vPVNyT1H0H7enhJNCyKH3HXJKisA2zauZLIalFHv4O1b2TZksjm475cdOGQZQRBYeyQHtSQwtX9znhjWEo268hOoWiXy6m1jWPfSb3y9PI0PyhQWW34Ke6whjG9rBqBdK1dw97cJGTxUj8d4OdH+xYkcfH4u3if9OfjpIuIfGnvWttvXr2XeshUgyyCKBGgk7n3qmVpHmsmy6+mrvhQWy4ED5P/2GwV//EnZRGjjam9dljyMSB5Gmi9eBHa7S+GSZewpKRwfexWKrQlXZW9iXLpX2CbIuvxivkvJQSMKLMpxZf+cGhHIbeHVycZxbp7pM5KV67fws6Y1M9KdOPWtWZ1SwnLvBJqFtbrg8c+Fb2xXUgd/QvjKB4hJmQ0ps4kWr0ZRzh6e3RTwCAkibeBPhKy6HusbLbFLvhSrW6DEDiZs/J2IdVAgrrHw/MMPwmdfULhzNubYduj9Tqdl/21zEi/P24/VcdoM2DLYgz/v6YPXeZxi1Wo1E6OtfHPcF4vZjEMR+O6P+ThpRsu2rvPDVGojRBDJUeQ6TTV/OSOKInHPDGfVW3NISktl71epCIqMyWTCarOjNxpQiSKiJHEyJQ0kVbnD/qRbbkNS1f7WUd8WloLZcyhcsBBjnz54jR2Lz4TxdTKuIAig0ZRbh0WDq25T5ptvIXl7I/p4E/7uu4j6qstMuLlw3ApLI2HK3hMsyilEJwqEaTXcFh7AlPAAInV1k9Le0+jFA952njDBIOtx3uvRhxHbCxixL4NPcjMY3H5AncxzNsL734yp3SAKj+8gdMHNl8SSEEDYoBEU+MzDvuI9BFMOBnsyPvufIzdhJkqPqfgPmICoa/r+Fj6B4SSKzeiaNoPSj/9mW8+36TLiFp76aw9/bk9FpxZ5Z2IHBrUKQqUCX0P1lbUxfbvx4fE0Vq5eyfT12Zywh3J1YArd244CIDPPTIZSFi334y7uv61pK7qNhUMb97NBfRiNoGBPLkQRJSRFRkIhp6gYp0oNioIIjOh3Be27dSc/N5eQiP/6otWOuXPnsmTJEq655hoiIiLqZMz8mTMp/OsvdK1aEfndt3Uy5tlQhYTgd9ttOPPzMe/Zg23bNhI6d0HXrh1Rv/zsVlzqAUG5BLL3FBUV4e3tTWFhIV5eTTPrYsi/uwD4p3MLevrU3Q3O5nCyLCmPUdH+iKKI2VyCXu8aP6s4nylrV2MX1SwdeXFSnucd2Yrfr0P5nmswRo/j2tvObopuquSsXYC47Dn8xBMUKM1Q3TEPj8im73tRUlzAyb3rcf77NpG2YwxV/UBuqY1ofwPzHuyLZ22TuCkKI1/4jhSHNyXo+X2Mlh59KxZN3L0ng3EzthMoiGx+fcRFizC5VDm25SAzFvxBuD6QKU/fU6W148xbQ11bQ7Zu3UpxcTFr1qxh/PjxdOrUqdZjKbKMPTWVnM+/oPDvvwFo9u03eFxRvTDtukC2Wsn7/gdKVq/GvHMnAOqoSGLnzUOohWPy5URN7t/ub30jYXRZHZo2HrXXyu+ct4frZ24vf29zyrR9ewX3f7WFlm+vYPgvW5hxrBCzw+U/YtB5kp1vxGK5eDqr4nSt945lOUHmfRdt3otJQL8r8X1lJ7njlmIgF+uPNza0SHWCh6cP8X2uJC1oAAYslFhtTOwSzqonB9VeWQEQBMZEy5Sgp7U6k+5XDKnU5NS98/l+sW5lpQ44sG0vTkHmpoduP6syIghC+U9d0717dwYMcFl1TyxdSvbcuRQtXkzxv/+iOGvm35b3/fccGzacwr//RlCriV265KIqKwCiVkvAPXcT9dOPhH84HZ/rr8OemETR4sWYd+++qLJcyriXhBoJr8aFsyq/mPE7jrCwa0t0Nayo6nDKLF+fDECH9H/R61QMjAvEUezKW6Hx1HD4UA7T9mUzDQiO9CI7qxTZ4iRAb0YeK9e6imtN8GnelRNR1xFx8m9iVLn1Pl9DIQgC/p17kr6iP6Eli7EVFqDx9mlosS6YhKOHCcrZjFZwkDBtdJ2VfZgwdADzE9fz/MjYKm+Qv6w4ir8gMGrohZUtcONCp9MhKAJWiwVNDZbv6hJBEAhNS6P1mrXknLE96rcZGDpXf9nPWVCIKjCQ8A/eRxMTgyrgwn3+aougVuM1ciSqoGAKZs5yRRSpVLTetRPhAvx+3Lhwf4KNhAidhrsjApmemEmx01ljhUUSBQStiGKVKco0UQTMSnQVlbt5dEum9Y/DYncw+rftnEwvJr/Qgo+fjmuaKXy5Gd796lvaBGo4mmNl8qiBhEa1rIejdNUXipnyFeYXF6PVXPprvE59MPZiHVKZg15TZuV3LzA46XT+FdluRdTUTRKt8OiWLJ5W+ZxTFIX3fv+DP7OM3N9Mj0bjvmTVBS3iW7E+aQcnth+mw7DuDSKDKIq0BWxaLW1Xr8KZns6JCVeT9f77qPz8XcpwmfIqeXoQ/OyzZ/ULEXQ6DN0b5jiqwtClMy23biH597/5dMEeWq09zp2D6ueaejnh/vY3EpyKwjcp2fTyNhKoqbl5XRAEbhnagh8XHAYgINKTnKRijF4aXr4iFgCdWsXKW3pW6uuR9z3Tj4QgJ7qUpPU/rOWvl+r7y6VwOaxIqvMOkK/tRNB58pY0drYvn8XgpI/ZEXEzsaMeRusfga6OlBWA1LQsREkiNPh0yHSxqZB7f/yLdYnBjIxezq19L34OoUsNS4GJHcs3cehIAqIiENejbYPKI5nMONVq1D4+qAwGvMaOxZmfj2wyudYBFQVnYSHF+/fjd8stFxSefLGRPD356XAJf7QcDEuOuBWWOsCtsDQCnIrCa8fSKHbKDAuofYm6V/rFcU/nSAZ/to7szFKen9yBG9qEVUjWVRUP3jGF8ScSQG1g1erlvLQ3iH+X/sPAoWPrrQCY0ISz3NYEp9Yfn9It2Arz0HjXPpdOQ5GWkkjSP9PokjWb/YZudL794zo/J3ILSpjx6J2IyKh6jOaBR++lsCSDm76ay7H8QB7tNJ/4oKXkZ4cRRN86nftyIj89h9nf/U6SPQsPQcegtn3QezdseQMpORl7mRIiaDSEv/tOpTalGzaQdPsdnLjmWtQhITSfNxdBrUZRFBxZWcilVdeiagzkaTzBtSpP37ddhUh7xPjxwbWdGk6oJoxbYWkEPH4omZkZeTwaFcy9zQIvaKxQDy3vTerA/V9twWRzYtRUL7lTsxhXLpZxY8az8NifTFkZxiPHvuCRe++7IHnOjoJyGeTT0A15EMO8K0lZ8CMRNzza0OLUiPSU4zi/GUU8RWyOvJNu1z1XLwpsXkEhIjIlhgA8tizkw4/UrJc0HC8I5Jub4+gW3IcNO5disSTV+dyXKg6LnZW/LqS4qBhRJSJp1exM3Y8gCFw/ehKte8Q3tIgc+2cuXpmZmIYNPWc7fZcuhLz8Mqbt2ymaNw/T1q0IGg35s36naN48ADQtYi+GyDXGbHc5EE/tE4lGq2HLiTz+3pHKq+Pi8dC6b781xf2JNTBWWWZudgEAKqFu7A7eZUpKprnmGRi9vH2Z8cKdPPfhl3yeGMojdguo677Yl3CZLAl5hfgAIBQ2rZvtjkPHaPFbX7wEE4lX/YFnsZn9M/6HPnsXAY5MzIKeQsmf4vib6TbiRnS62vsjWbIzAIjrN5TEw4fRbPqHksAhvDrZk+6hYSRv/wnRYUCjvjBl/nJi4z+r2JC8ExEBH8mDQkcpGkHNbTfeQmhc3eRRuVCy//4LlZcXHZ966pztRJ0O3+uvQ/L3o2jePJJuv6PC/mbffIMmKvIsvRuWY04dSPDYoBh0nh58s/Y4W07mcccPW5l1d++GFq/J4VZYGhitKPJ3pxbMysjj3ZMZhOnUTA69sLJ4oR4uBeNkRu1Mpfk5GazONtLTI7telBUXCpdGRaFzk7t1LcEAWs+GFqVGHExMIwgDXpiImnsNUUAG/qQb23I8oAsqWwG+xUfpuPNx8ne+zJ7wq2l51RP4BNf8xuHr5wNA0p6dhLfxIPsEjMhewaTe/5C46QeS+ASV7INvWGX/KzdV4xvoBwehjW9zrnn4Zooy85FUEkb/xpOnSnUyEaeXJyp19Xz2PIcOJXbxIhRZBgQQQBUQgOTZeL9bRyUvvG2l6Dxdua9u7h3F79uS2Xwij4SMYlqFNF7ZGyNuhaUR0MFTz2GTge9TQV8HJnd/nevPerzQVKv+/67fSKrsx6831Z9DnoBSZyGxjRVTegr+O18gQzeA0JtfaGhxasSNI/phGXiQ7RsWgMNCaLMYQlv2IOQ/52fa4R0kLv+C9im/o//sJ3b4jYBWo2g/4GrU+vPfHJ1OJ58kO3F6RBCSnkB2etl2SeSvFb0IdkigERgwcvu5B3JTgSMHXc73fUe4cp14Bfs2pDhVIg4bhvGXXzjUpi2lo0fR7YMPztleEAQ00dEXR7g64MjuBABGGk9fh7UqiQmdI3h78SFGfLiG26+IYVDrQPrFua2H1cGtsDQw+0vMPHggkQOlFkYHeHNVkM8FjTf8+00cTnDlN7m9Z3SN++dnpfHl9iLCRBVRzerTzKqgXOIWlpJ9GwkS7GhGPotYzafIxoROq6HroAnnbBPWsgthLb8iK2saG+d/Ttek7/DbtJiiTc+xO/JaNK1HoBIU7A4ngiCgVQsIopqTJ4/jd/Qv4i07eAotyzSt8fMbgV4fTrQmEptiwmvzE6S064ZO23QiQxoLfr5+kA1BceHnb9xAdH7uWQ5FNoM33sS4cBG7goPp9HTtKkA3RmbM2QQEcO3Iijll7h0Yi7dezXfrT5T//N/kzoztGNYwgjYh3ApLA3Pr3uOkWOzM7tyC3nWQkj8116XNf3lPT0ZE1zyB0hNfzSHDEcifNzZHqGU11uogCJf+kpA9JxUAz9j6LS7ZGAgKCmbY7S+D8hJHD+8nZcmHdE/8FWPSd1W2bwkc17RiXdRU9pqKGOaTj09mb/K1MlhBIxjANIX4XR/h9LNg3TICQeuJ6ubpiAGNwwejMfNvwkbUNh9mfbgMlU6DSi0S1MyHvqM6NbRo5QiCQJtbbqFkyFD23303Hj/8SMldd+Hh1/Si6f6LqcTM9+YArpCz6XpF5bInN/SM5IaekczdncZDv+3kn11pboWlGrgVlgZiT7GJ/0vMIsVi56XYsDpRVgAGtQti/upEpn67lfVPDSLCs/o+KF/98B0rSqJ4tpOFlvFd60Ses3PpLwkJ2QcBMB/dibrzoEv+eAEQBFq0iqdFq29wmotISTqKU1HQlFWuttidOOx2IoP9aB7YnObAUMC6YQXZc6HleD+sW7ZgKTRgyeuOVe6BuigFNEakgm0onwzAqo4FQwCqG95DCm68FoSGpKtfT5IOaskrdAAOkEXSdmbRd1RDS1YZj/AwxLAwxKNHES+R78jJg8cAmNz13EUdr+oYxt87Ulh+MJNn/t7L/YNiCfXWI4mXxudQ11z6YRqNkHX5xQzfdpj1BcW83TKCqRcYynwmH49oS7MobwS7THKptdr9kk4c5o1DwdwcmsJd111dZ/KcDdfX8dI+/RTfaGRFxGvuBFI/ntrQ4lx0JL0XEa26ENW6K6HN4wltHk9Mq47ExXdDG3g6xb5lzWpKVqcAkDfrKEUnWmLLc13oLYYxaF5Yi+bpxcg3L8Lu2w9Bo0FVuA7nD1Ma5LiaAs0CXcU27/14KA98NpyIbloao0Uz98gR8pOSsBcXI4viJVMnqlXHVnjYTGw8knXeto8OdSWU+21LEn3f/pfXFxysb/GaLG4Ly0XGLitM2nWMrl4G/urUosYp+M+HKIoUmlzhzIE1KEjn5x9EgLiDfblgMVvQG+o7bf6lnzgu/OZnsOTdje7jGFBd+mUIakvOQhFwKTBOAjBISzF0aw6+UWi7jS9vp27RBvXDPwJgnvUp+oPPIuemIPqf+yn2ciNlUzrr16QBp1UUV+XlxvWdS1u3jsI77wLAFygJCkLnXfvEmY0FRVFwAsFY+bXEl1etNiTt2TNdd2zmw9bnhnI4s5iX5u7nrx0pbDqey/2DWnBlh9CLJ3gTwK2wXGQ+OOnKObG9yMSKvCKG+XuhqcOnCqesUJRtBiDaq/rLQR5ePnx7TSwTZmUwZ9FCJk+s3zToAgqKcGk8TZ0LrY83TkWFqGp6TrcXG6/mR5EMTgxHPkHQ3gUDz25BUXUfBgefxb5pOdorb7t4QjZSji1LZMlfxxAAuWxb925BiKqy75ji8hk7vCcR/2Bv/IN9GkbQMzj588/4AkXdu6ML8Cdg+PCGFumC2bJqB8/OO8hRtQ9oXJFZBYUl+Aed2y8n0FNLoKeWJ4a3ZP3RXH7elMj9M3Zw/wx4ckQrrukaUVY5G0RBQBRcf9ISq4Nmfk2/Tll1cSssF5knYkLIdzjZVWTijn0n8VFJdPc2MiU8gEF+nhdcyl0AvAP1FGab+Wx7Eg90jay2mbV5y/bIZPHFjlIm13vZlkvf6RZAECWKhCh8U2eiON9FuAgVsZsaEW/1K3tV9vvVpyFp4zn7qJu3xCFGwv45cJkqLE6bk9StmZzclsneg/kAxLf2QaNT4R1qpM2409lfVRoJQZFY9pnLt2LoPbG06hRV67mPz59PzvffoygQ9sD9+LZqxf5XXkEpKUV17BgOf3+iXn+N0HNUXfbYuAmnJNHjpx8v+LrXWPhgznaOaUPwsZsoUBtobso6r7JyJiPjQxkZH8qNvSLZejKfF+bs490lCby7JOHsc17bkbggT+LDvS6Zz/FsuBWWi4wkCLzV0mXCPlhiZl52AbMz87lhz3FGBHjxdEwobT1qv3wgigK/3dqDMV9u4IO/9vPBX/s5+VZlL/WqKCx0XfQ6GAtqPX91ES4ThQXA2vNpfDdPJW/XBvy6umvhnBdJA3bzOZs4c3KQnCk4xTYXSaiLjyzLHFl4Emupo8r9J/Zkk5J72k/tysktiR5Q9fLY8Ot6cKjVSTbMTES2Siz/OoFWn9ZOYdn4xJP4zJ+P08cbrc1OyX33UwKcyrqT37kTnnv2kvTwIxh//YWMLVtoMWFChQcnRVFQ22wU9uhxydxkS4pK2akO4H6/Yp54+ga2r9pG85bdajVW6xAvWod40T8ugBM5pSgKyIqCrLg+O7nMuvLEH7t57PfdAPx5T2+6RTf9CKtz4VZYGpA2HnraeOh5MjqE+dmFTDuWxlU7jrCieyui9Npaj6tViahVIlagZevqZc3dvGE11811ZcZ9ZNK5a3vUBcJlUksIIHDgWNgM5p2LwK2wnB9FBvHcIfWiry92Q080xUux79uKOr77RRLu4iDLMsvf3s6RxOJzthOAax7uiDFAjyGw4tKAoihknihErVWxf20qiftyka2uz1UR5fJ5UlesIKRvX9T6aj4oFRQA0GX5co7MnAnvvV++K3TpEtpERrL9hRfx+uMPUoe5lnl2rVtHl+nTy9vlHnQ5liqm2iW3bIwsW7ABq6RhxEBX3qCuA2unrJxJlL+RKP+zF6jsHxfA4cwSbvp2c3ndoksZt8LSCBAEgbFBPgzw82TI1gTuP5DInM5xqGoR2vbZziTembUXgCt6hvPrhE7V6ndKWZnWzUpsy/ovOX855GE5Rcb8HwkHFNXls9Z8QSgKiOf2+REkCfUj/6C8EYUy92kcfj+gCmuc9WRqQ+HxwnJl5bpHOuETVXXWYEEtIqkqLzMe35XN+j+PUJRjOd1WFPALM2ITiyhJ0fDPV8sJWvQZPgcPssvXF3nIEMjLQxEFDC1bYoyKJmrEcFQ6ly9cwq+/Ypr+ITq7q/ywYrcTf+ed7CgoQM7KovObbyKV5W4Ku2osKatWYfbywjMjA/2ixewICERSSQgzfkNtdVmGYp59pu4+tAYmr9QV7KD1uHjf8yAvHVaHS/n8Zu0JFu5NR5bBU6fi2dFtEC+x8Gi3wtKI8FJJfNY2ivE7j/BoQhIfto5EqoEVYkd6Ie/8tQ+Adp2C+f6qDufto8gy3/70PRBCZ30WN0+6CKGiyqlohUvry1QVitOJ977pFBFK0NX3N7Q4TQNFBun8lyZBq8XW+QW0u57F+tfLqB6sOkldU0SWXd+RUdfFEdC6Zmb+Zd/v5/DmTBAgpLk3XoE6DF5a+lwdiyAIbFy6hx0pOeSsz6X54cMUXNEHISsb7z//xKLXo7VYEJavwAZs/vprevzxO2q9nuL9BzCWlFAUFYlu/Hh0vi6n0i5PPFFJhtDu3QlduwYAh9XK1ltuxefnn8v32/U6xAcfIqRLl1p+Qg2LLCuVlAEhLwcIoKSo9KLKEuipZVCrQPJNdgpMNoosDk7klHJnv+aEeNdXLbiGwa2wNDK6exuZ3jqShw4mMSrAm9GBPtXu+8zSgyAIrHxmIM29q6flnzyewGuHQojXZfPDfSPYumkNAWHNiYmsx1DRUwrLZRAlJMsyTkWNt5iCxWoGfBpapCZA9a1vSsou14t2V9WbNA1CLXT67ORiNs4+RvKBPNRaiVvf6oNWX9lSJe09gSDrUMlWwv7+C/9WrkzM2QkJ+MXEIGk0FCclkb54MX4fTGfPkKHIAwciLFkCQOhTTxM5ZHC15VJptfSY8St73nsPe3o60bffTlCH8z9MNVbm/rGSh7abGSrkcE2XcBIzC1mSWMp2KQCDw0rHHu0uqjw6tcT3U3qUv199OJtbv9uCrDSuMPa6wK2wNEKuDfHj59RcZqTnVVtheWt5AgkHczH66aqtrAD4BzcDjrPPEkjH93eWbd3N881nc+fUB2sse/W4fCwsklqNpccTeGx9Assnw5HvmochIrqhxWrcGAMhYy84rKA6uy9XQfYBbNHt0OZoENa9idJ/LIKqsu+LoihNzrHTaa2ZP8Lcj3eRfCAPAK8AHcPvjK9SWQFIPFyMxu6g/+TW5coKQOAZrz0jI/GcOpUj/v44vvgSr7//BqCoezeiO3Ws6eEgSRKd66BOkGXNHzjXfItq4B1o+15zwePVBp3kOpeWKwEs324FdCDpCLAUMveh/kgNHAl4yvDjVljcXDRaGLUcLrWcv2EZo9oE88Xyo5QWW2t0gfby9OD5+Dwy8ktJKlZQ42RBUQzZSj2WPS/7Il16X6eqCR4zhZJW3dD/PJzc2W9hePCLhhapcTPkJZhzDyx7EUa9Xb65MP8EP/z7FGnmHCyKk7UO1w16UoA3T+QcwpmUwM7CfP488DuHrEfwlXwQFNjJAbSKmg604ePrv0Cvb9y+RIfmHWPFgkQA9D7nN+nvWJJI8oE8AiI8GHFXO3yCq3bSXDvtD7LTrGQJYXgJmcSM7lFluzOJmzgRJk7k8IKFaLUa2gytf4f8/2I7tBXnoY0oOUfQJf6MKCkoSzdinvcc0uQv0XQadFHlGX71IPzXzSRX48lHnTRsPJrDnZN6ExEZgs7Q8EswW0+6oj01l2AKBbfC0khRCwJ2ufq39I7hPigiCPaaqwF33nRz+es/5s5jwQY4mmvD6XAiVfHEeuFcPktCp9BHtUIQLGiKDze0KI2fTpNdCsvOnysoLGt3f8c3xYdo5lTwQOB271bkWwv5zTOTUvzIXf0O68XtBDsC6KbtRK49FxS40/tGEAS+L5zBtNnP88YNHzTgwZ2bQ3OPsWJhIgIwcEQkoZ2qLtuxb00qeemlHNqYjt3issZM+l+3Kh1wT7En1R+dvRBPshl1X6caydXyytE1an+hWHf8i/PgaoRdP6LV5aEpuwyZ7BFoHliM9e830KXMRPx7PKb9D2O48dWLJltOcgYBthJyNZ6MmjCAcefIYtsQeOlct/XNJ/IuuYKKboXlEiI42pus5GJSiy1EeNUul0vXVtHEbt7GiqJwZs+ZyaRJN9axlLicKoHLYUnoFPaiInSCjC3o/E+1bsrQVLTymW1FCIrCgtv2IJTl9HCYC/j9937M9fTAIO/nTu+buGfUw2i1lZ90/Zf480bGB3RZ9SuTBtbDeX2BpG/PZMVCl2Vl5DUtaD6kctRT8sE8Vv+WQGGWK0+NSiPiF2akde/QcyorDosrKqdDe4nuj1xXD9LXHYrTieqvCWjVClZ0WPS9UY1/CUHviSEmHgDVvZ/jzPwflg9Ho0/4CPM3Oehv/wTquRZRdlIaAz/aSKlHKIPIQdPIlBWAdmHe+BjUPPP33ktOYbmgv+5bb72FIAg88sgj5dssFgv3338//v7+eHh4MHHiRDIzM885jqIovPjii4SGhqLX6xk6dChHjhy5ENGaPFZZIdfuwOKUz9+4jE/HtUcBrvx+M4VWe63m/WHJRo45gwAotlZ/7hpxGUUJnUIXEEChHIaq0G1hOS+nzo+oPhU22+wmdArlygqASu/Dw353M0zVnzlj5vDw+KerVFYArht2KyOEAbx14gMSTu6vN/Frg7XIxu5/jgMw5MroKpWVxV/tY+5Hu8qVFYBb3riCyS/2pPOwc4d0/3z/PIB6spjWHba9axCm+SGpXeeAZtoJDM8uRtO2N+oyZeUUUnAUuhc2YbUFoE/5Fevzsdi2L6lX+aa+u4BStY7b/Ut544FGWPoa6B3rz0OD43DWwELfVKi1wrJ161a+/PJLOvzH2/vRRx9l3rx5/PHHH6xevZq0tDSuvvrc1X/feecdPv74Y7744gs2b96M0WhkxIgRWCzV9+G41Bgb5EOq1c6grYfot/kgLx5JJd9edcbLU3QP9uaRMa0pTC/lik/WIss1VzjsdjtRqgLW3B3HbZPr6ym0zIeliTlCXggF+3biLaZh92u60REXDbnM4fQ/yeOsdjNVuZHeOfYBPrjxU0IDw885rCiKvHz1m4TIgTy28jFKzSV1JPCFk7ghjWNZZtQCRPev+FR8cm8On927kmM7XJV/b3q1Fze92ou7PxmI3uP8NarS1u/HpPbDz5lBu5sH1Iv8dYVclm0bwNzqYQTduf2NBIMn2mn7KQ28AY0qD828a7E+HUHpS/0w/68NlufbYfnnw7qRzenksMYXf2sxzz8+idCIoDoZt75QUHDKSlnhy0uDWiksJSUl3HjjjXz99df4lsXiAxQWFvLtt9/ywQcfMHjwYLp27cr333/Phg0b2LRpU5VjKYrChx9+yPPPP8+4cePo0KEDP/30E2lpacyZM6dWB3UpMNTfi2ktwnEqEKBRMSM9l36bD7Eh/9wX2Ud7xjB1bGtKss3cv/hAjedtG6QjyeFFSGizCk+ydcpluCTkSpQHurSVDSxJE+DU+SFWXLG2OsxoL/Cc8fDw5P0r3iVLyOWFOf9rNBfzUzk9rn26K7r/5M5Y9MVeFAU8fLVMebcv3kEGvIMMqM6xBHQmc793LTO17uKD1tujbgWvAxRZxrZ3HebF36CkuvJIlWr6op9cPb8UQa3DeP/nOG5eRanHMGTBiMpyHFEpRbDlo9v5EuZ5n16wnItnr6ZUreeDYc2qXZ+todCoRCx2mdhnF3Ltl+euy9WUqJUPy/3338+VV17J0KFDee2118q3b9++HbvdztAzPMlbt25NZGQkGzdupFevXpXGOnHiBBkZGRX6eHt707NnTzZu3Mj1119fqY/VasVqPV1Do6ioqDaH0ei5q1kgdzVzOd2dNFu5/0AiV+86SqdjCej8/FApMipFKfuRUcky6rJtzhAvFq1J5OF1WzlVWF5BKLtAC2dsc3FKhTghG1AEkccffJVnStefFuZs1/WzbFfOTKWh/KexoiAU+SAHFtTsA2nCeLfrgvN3NRZ8z9/4ckc5ZWGpeHnKtZfgzYUvabRq3Z7/nXiEl7PeZuaan5g84NYLHvNCUcqWfp2201bRvPQS/nhzG7LT9b259c0rajW2LKnpGJFH53snXbigdYicl4H1yxtQFR1Co62YbE01cGqNx1PHdUb9xJ8VtimyjO2ZcDSbnseUvAfDfV/WStbDuxJ4YmM+neQi+o1snEtBZzKuUxhGrcSivRlsOZlHVpGFIK+Gj2C6UGqssMycOZMdO3awdevWSvsyMjLQaDT4+PhU2B4cHExGRkaV453aHhwcXO0+b775Jq+88kpNRW/SROu1/NyhOc9Ne58ioxGnxYxTq8UqSpSKIk5JwiFKOCTXT5CnFWu2nYRSl9Yg4FJTXO9cvwVFQRBwKRBlbTwwESk6CDWVYMs8Sx2TajzknnW1p2y7KcmAkGKr/gdwCVCiiiTcvoqsNYsI6t/4L3oNhqPsYeQ/CkuKvZAIsWpncrPdxOHMtQR6RBHm0/q8U1w98kZ2/LSdd098SIfozrSLarilupRN6Sz950Sl7fM/2YPDJqPSiIyc2r7W4ysIaI3nXzqqS5zZKdj+72rU9qM4FS+cGBHaXYXuxtcQRAlFlhE/boUesNp1UJZux6TtDSHx6HuOqRM5BFFEemQd1s9vxpA1E9NbSeifnIdQjUzKZ/Lr/K2Y1L5889iQRm9dAfDUqZnQOYJCk52lBzK56dvNLH20cS8HVoca/dWSk5N5+OGHWbZsGTpdw2lrzzzzDI899lj5+6KiIpo1a9Zg8lws/NQq/u/B2zk2ajTa1q2I+PhjVH71VZ3z5vM3uQD2t2mPor20K4v+F+Ga7yiYNQXfFbfh6HQAlVf1ClNeLih2C1n712D0CcIDQDrjJqsoHHUUE2KU+HHNVeitRxFRsIlGVLIJo2BFJ0KCbODaoXvPO5cgCDx/9TQOzLyOx1c+xu/X/42Xvup6PXWF02pn46c/YTaXoNZoESQ1TiEIpyUEgAHDmuHbwru8vanQpdBPeasvGkPtAjpP+bHt2CcQtGQbUSMuvCDf+VBkJ7b3BqPXZ4IWrCYjEsXojn2G8/kvcDg9kJ0Seg+wmj3QvHYEdAZkUzEGQ93nf5JCY9G/sp6S5/rgwQasz7dAunsBqujqZ6Tt0jyQH/c42Lt1H4OubDoFTG/pHc3G47msP5rLv4eyGNS6cfvdnI8afQu2b99OVlYWXc6o/+B0OlmzZg2ffPIJS5YswWazUVBQUMHKkpmZSUhISJVjntqemZlJaGhohT6dOnWqso9Wq0WrrX0146aMKjCQZl99RfLUqaQ88CBRP/6AoL64T091hW3xP2zbusXlfFuWk+XM1yCSrE5iWXcD2QEuBVmpkHSuqv9Pr1Gdfq1UWKJSUMqan3L+db2s0P4/a11KFXOde4/ynz1l20K05GsDeWnRS4y/7pNzfDqXD46SPFb89jFbUh04yy5J7RiN354U5MT/UWhVcCh2HmyhwcNYhM1+gBwxEEXlg2zPQ9JEoBjjSC/cRqCQU+159Z4evNf3XW7YeCvP/fMUH1/3eb1lxJVlmVXvf8Gu3UvQSkYEASyOUgQpEK3XzagFaDUyqsLTe0AzDzJPFKHS1v6JXhRF+naxsW6nB7lHM4kaURdHc27Mb47CoM/EFH03uon/w+jph6IomBZ8jW7z02g1p5fwxTsXlDvWivWgrJxCEASM09Zimv0hmu1vYPvudlSvbq52/993pCFIAbTv3JKUY8kEB/uh9jh7FeXGgigKXNkhjA1Hc3nhn32sa139kgqNkRopLEOGDGHv3opPL1OmTKF169Y8/fTTNGvWDLVazYoVK5g4cSIACQkJJCUl0bt37yrHjImJISQkhBUrVpQrKEVFRWzevJl77723Fod06WPo0pnIb77m5I03kf7Ci4S8+AKioXFn7/wv1ivGoCQnItitLj1CUQAZl2+q7FqmUhTiMhMRCwR+v6EnQPkN5cz/Xa8EXP+EM7f85/WpNhX3C6cWxISK41b8Xyifv/IMFeeuOPOpLa5XDqWU9coaXrCsZlhpFkZj037iuVBMual89cl0ihQ9zY1WwoOM7EosIFFuRopVwZktYJMFnJKOPsYirPRj1ODvq1QsZm2+G6VkRY3mj23TludOPM5z2a/z/dIvuX3EPXV1aOVkHzzO32++SIm1gO7dx9H/ibsA+PjWO/Dwj+SWt/oDoNJUvBwXZpuRVCLiBWYsbXvTYNbt3HBBY1QH2VyCI2ErBvtmTHIbDLe9U75PEAQMY6Yi95+E9cQBBK0G0eCFOvL8y3d1hSCpMEx6gpLEnRgL52Ne+jP64dWzJPvrJRS7SP8P1mFSaelizeK3t29Gq2n8qcyu6hhGYk4p7y87zK+bE7mxZ1RDi1RravRpe3p6Eh9fMRbeaDTi7+9fvv2OO+7gsccew8/PDy8vLx588EF69+5dweG2devWvPnmm0yYMKE8j8trr71GXFwcMTExvPDCC4SFhTF+/PgLP8JLFH2nToS+/hoZL72MLTGRqF9+RpAad46FM+nyzZvVavfv16/Q+v2ZPNlyDF36njs8vqmwcdtnTN3/OUlJ62jT5tI4ptpSlHmSAsWDsZ1D6TrubgAGli1jnIpSc9pt7N30Lbl2CA8YflYriI+hGapShT+23Me4Lu+jUVUveeLYkdexe9YePkr/DJ/lXlw99IY6OLLTbPppBg6nnaumPEXcyP7l2+2WTERVdCVFBVwWGUuJHb3nhVtPd321BPA8u19ZLSj97knUCb+goMapDkFlT0ajN+FUYl1aeuuqfbRELz+0HRt2ScX48PfYnw5FWvkkjg79UIVEn7fPu89Ppucvi0nNMpGYncUCbTPWrtjG0FGVA0kaI4NaB/H+ssM8N3sfW0/k8f61nZDEOjwhLhJ17j00ffp0xowZw8SJE+nfvz8hISH8XVY46xQJCQkUFhaWv3/qqad48MEHmTp1Kt27d6ekpITFixc3qJ9MU8Bn/Hgiv/ka865d5P/6a0OLUy/Isiv3TP6O6ptvGzsmq8uZWaP1Pk/LywHXJSi4WUz5FkEUK4TUS2oNkq/reqFRn10p79vqATLEKPxKlvDP9uoX7hREgWeueZWRmkG8kvIWfyz9uaYHcVbWf/wDh49vok18vwrKitMpI4he6D2qtoyKoogoCSgXmPzLabWzJcETlcOMf1zVy/I1xfTdkxiTvsIp+eKUApBsadjVzTCX+qEXjgEghTSvk7nqA0GlgRtnolJZsH88Frk475ztHU4ZlUrixjvGcu/UMfh4GxBlJ2G+TceqHR/uzaonBqJRiczZlcZVn6yj1HruvF6NkQu2Z61atarCe51Ox6effsqnn5497v2/uQ8EQeDVV1/l1VcvXj2ISwVD9+743nADWdM/xGPwEDQR506e1ZQozEvH64u/ON4lhJH3v97Q4tQZYQZXqPrBtE3ENh/SwNI0NKcci86d5FCtclUSNhrP7lxv1PjQPu5pTh64H1tpzTIKq1Qq3rj+A8RZTzAt7V2cS5xcP+K2Go3xXzJ2HmLLhtm0jO3FgMcrLjWV5heiyEVYSvKr7FtaYEV2KviEXNhN8VRK/p7dBCKHdb2gsQBKv30KQ+JXWIhEN20HQpljtBZw5mZi+upWCInHMLh+nfYvFE2XYZTufxh9wkdY3xmC7qUtCKqK1qyvFu7mjTUpAISY89Ehk6rxxi75M0aVTdteVzWE6LUmOsDIhv8NZszH69ifVsQjs3Zxz4BY4sO90DbyDMinaPzxWW7OS+CjjyJ5e5P+zDMNLUqdsHfdP8x5bCL7Rg/Dw6zQ6qmXkWoYhtiYadlqPD1kDc8cm8n0Pyc0tDgNy6l1irMkcEvddYI9ry/F8btExPbHEeWz125JyttN4sEHsAtq4ls8dtZ2Z0MSJV677j2u0o7g9Yz3+XXxNzUe40z2/LMQraRnxPOPI/1n2ccrwJWPx+gTWlVXFn2xB4CeV8VekAxOS92lDjD/8QaGpC+x0ALti6eVlVNI/sEYnlmMYcp7dTZnfWK8+RXMMVPRiccxvzoA245lyObT+WCy8k6ndcjQ+yKKAg+GWvhpkD8fvDi5IUS+YAI8tKx6ciCdmvmw7EAmEz/fwI8bTja0WNXm0rkLXMZIHkZCnn+OlAcexHLgALq2bRtapFojKzJHX/4frVOg1CCSPLEXwzs1/fwBZyIZ/GhjDGOL+SS5jtLzd7iUOY/CYl6Wgl+xHqtGQJvbHoPj7H4paw+8TogoE93qA9qH1y7PjSRKvHrd20i/i7yV+RHORU5uGXV3rcbKTj9JUGAMGkNlmXNT0gHQeVReFrSa7WSedN0sI1pdYKLBss93/S4dAat2EzGwY+3GcVjR738bRQHdM6srWSOaKsY73qH0kzz0GX8izp2E/LdIacTNSC178/TYkXyz77Sz8tJ3JqNSN/1bpk4tMXNqL1LyzVz35UbMtnqqGVcPuC0slwjG/v1RBQeT8sijmPc1rsJuNUFAoLB5EMU66LRtN8Nf/76hRapzFFlmU0kS8Q54ZdKChhangTkVfVW1wiKUXUt9rnPd2CXp7I6C7aJuAmD3wecvSCJRFHnp2je5RjeWd7M+4bsFn9V4DFmWyS1KJTAyptI+RVH47cVXABU9xw+rtO/n51yp1IOiahbmKztljqcl88Jfb3LlVxPp9m0Pei7qT4EhAYCS9KqXn6qF5LJsCSII+saX3v9CMD7wDcqjCZg7vIjV5ocx40d0a+5Bfr0N1ysrAIVYW/4loaycQqeWaBHkgSgKdeqMXd+4FZZLBFGjIfLbbxA9jJy8/npyvvwKxV67is0NiSAIxLTqgc4ONuellQl37/7fefbXQQz/oQMJkswQv/ZIl8iT6gVzNgtLuIiN6jkHCqIrN5OgjbhgcURR5PlrXuN6/Xim53zO3ytn1Ki/rbgUu2zFJ7jykk/ywRNYS1PwDe9EYGTFDN8bZx/DanKg0Utc80z3as+38eB2rvzuasYtG82ckhkkaQ/TEZfPyvawzaDIGAIvwMlbEDDZXY601q1Laz9OI0XyD0Z/9ePo3zmG7ZpFmGIfRJG8eUv/LXu1d/Kxxzts+uN9LOZLyyJqd8p8sOwwO5MuQJm9iLgVlksIbYsWxMycifeVo8mePp38P/5oaJFqhaWkELUTVl43lOVvVT/ao7Hz2fbpzHPkMMyrBd91fJQ7xl+akV014lSSwLMoLIIgoEFFzoqySI5zBM346gIxySKBtn38sGoYuaXJFySaKIo8M+kVeiqdmHFyVrX7FZ5M45/nXQEEPs3CKu1fO8MVNTnu8fsqbLdbHexa7pL5+ud7Vnu+rQm7mLrlNlI0x7jR425+7DWDJSNX8MGElwAwKAlktfsC717VzwbuzM2idM5nlL7Yl9Iv7sP8dEuUMg8C7YJrUBpRpeu6RtOuD4abX0P3ZgKmmPsoVtpi0wbTY980it6OZ/vnd7DpvUkkbFve0KJeMI8MiQNg28mmobBcOjYuNwAIGg1hb7+Ns6SUglm/43v99fVXdbmeGPjYO6wsvZ/AFbvwOrAcx+M2VOqzO1s2FULVHrQyFfPUpDkNLUrjoVxhqXod3RDmjWNvNsa082cVjfTvgkeff1m192UCilexeOM4Jg/edkG1X0RRZGz0WJ5PnMbJpKNER7ao1MZWYiLvRAp2s4WSrBxWzPgCBYXhE+8nqn/FyJyinAIyj20iMKY//uEVkwbuW52KIiu06xuKp3/1Uzp8uPH/EBD4sd8MOseezpPlsNvoludDuraE3d4JDNi/nME9KxeTPYUzKwnH0V3Isx9Fq8vBKOF6pM3YC3qQHVmuca0ionJmesRLF8Otb2IAwoCUw7s4sXA6MZlriCCLnWtk6Db0fEM0am67Iob3lx2ulNm7seJWWC5R/G+7lcSbb6F42XK8RgxvaHFqhNbTh1Hv/MruFb/D/S+x7o+PGHjDkw0t1gVT4DDj7TZqVqT8rlf1BTNmUDsYBMefX4nGocZUUsC5Fjb8DBFc3fMb/tnxFCEFfzF3RR/8NlqxJ4cjqzSuMgwAgqtieXlUdVkSLVdpCNdvWSWiqETsOLn1eDP+WvgIzZt1caVlUJSy9AwKJ9P2VJDBxxDMtW+8hWdoYIXtiqLw+6tvoSgKQ2+vnJxu35pUAPpMjDvHEVbkRFIKe4QtjFFdV0FZAVCpNXz/6FqObFvO1fsfRXOOSteKzYL0WXtXCyOUeoxCiopH8QhFEGSEwFg0nQfizMtA8vZFUFcvKd+lRETLTkS0/BGAde9PpkXRFjJTjxMc3nhzzpyPEzmlFFscXGC6n4uGW2G5RDF0746uYweK5s9vcgrLKdr0v4ptPtPQTv+RwlE34+1bN4mvGopipwWj+tJyWLxgyutGnfuKae1lwHR0NXlZ+YTS57zDFpccxAPwlLKx9wXfr0xkZevKqpSXTV32+vQ2BbHM0CPKIMkKklNBckJibDQApcX5p8tDCCIIEOAZgbdPCN2vnohap8OvTSQqfeVaZ3tWbKEwcx9t+t9IWMvKfjbFeRY0nipUWhGnQ0ZSnVu5zcrJY/LiyUhqNTf0vOas7WyKE4C8nFScshNJrKy4WJb9wCkVpDTwGoz3Vx3SLQVUXuJqbDgspRxa9hNeITE06zaiXupDRY15EuG3SQhfD+HwuJ9p2bnpFEQ8k4d+2wmAn7FpWLDdCssljOfgIeR8+im2lBQ0ERfuiHix0ah1aN94DsN9r3Bw2R/0urZp+7OkOE10015gmOolhqKcO6z5FG3G9GLBwo+RnPtwOl9Fks59gQ3x70dJ2lE8JBuqPA0dftqCSlP74nraLz5h+b+LGTb1FoK7Vt8Z9kz2/bsagO5jB1XaJztlfjRYyJQUXn92Ee31Oua9dPakgluP7GLp9jWUagt4tvmrtG/R6qxt1XrXctpzOd9y4Ktt3DzqGUKbtam4VJZ7EgDrsB8wXtE0cwPZivNI3r6Emav2YUcNZMKCTVwZ70P3SY/U6VzNWnUh9+6VpHxxLYnzPixXWKbPWERBXg7jRwyic1zjv+aabA4mdA7n2m7V929qSNz26UsYv5tvQvLxIfO111HkphNrfybGYNcTXfHceTgdTS/q6RRZOYdIlUTiPJtu4bH64ZSicv6n4ObNp6BWl3Lo0PkdYIe2e4qY7HiwQ/v2n16QsgLQ5vqbkGSZ/X9W3/n2v5QW5qE1RhAYWdlSePJoAZmq00rbXrOFAlPlKLnsglyGfXMlt2+4mZnWrwG4ps+5M662aNuHH9u+QWShml/1uxm56noe/b9RbFw7E5vVhGIuRX3gKwA0vZtW9lZwhZC//PLLvPH+x/y8KgGtKHPX+P5c2da1eLgpIbNe5l088ytmC6PZIcfz9Kvv8L93v6Dw8GaEnGP89vfcepmzPvBvItYVcCsslzSiwUDws89QsmoVKfc/gC0ltaFFqjGt2/Xn6Oh4IrYls+TJG7DbLA0tUq1YtGU6iiDQOXJgQ4vSuCirFSVUI5Nxq5ZDMJWGkJxy/ugq2WqlxHEMbYEHfq0GX7CYGh8fwo1eHD+aUOsxZIcZ5SzOxcFlOVcCVSpe6+vyYbnt3XXsP5xbod29fz9Mhjrp9PuQx1Cdp+ipKIp06T6Wz6/8jo/CHgJgpU8aU4+/zqy/p2Fd9jUqnZNSUwRCFctFjR2braJid8u14zm8Yw0LDrjqT40eNhDF6azTOR2WEo4UaojxkgmK74Ng8IGSHEyCa2FNr28adYYUaFJ5WNxLQpc4XiNHorzrIPOttzg5+XqCn3gCryuvRFA1nT/92A/+YGHmEGIW7WOZx72Mntb0ksmlFJzAW1Zo22ZSQ4vSuHCW5VgRz5+PRhRFPL3G4HR+Q0HBCXx8KidlA1AcDra8Go95iEysWLsstVXRqs8Alq1YQMbmTYT0rHmV3tKCTFBK+PDmW3nk5x8r7Lvv/zYB8O5V8fToGMTO39/B6Czly4UP8nHLPhQUF/Hk7OdJkHbTUxrIV5M/QpRq9rwZGduJyNhOrMkeSVbmSW5dfx9r8tYxKC+TCAlUN35R42NqDOh0Oh6+6Uo++mUBMeocvEObczS9CPACYMbCdTgXbqStNhPBXoJJHcAJqzd3TxxMaPv+5x78LKz78VUEdAwdPoLw+CsA+GPFZvasWQwCdO/aua4Or15RFBCbkMbitrBcBniPHUPzObPRd+xI2tP/49jIUeT//nuTWibqPd2lpAh1/KR0segacQWFosDzs0Y0tCiNCluZxcwmV++i6evrKu5ZVJx91jbOoiKsrRTEfAjvdteFC1mGV7NIADZ983mN+yqKgodfJ5d8tlw+uf1ufnjiJRL3HQFgXU4RUSo1fbuGgsNOmDUDb0cxqhJXKPGbC6azSfmXq4038dl179dYWTkT38BmtIrvRydzIJt981nqZ6LE8yo0HZqm4yiAb4vudA6wccIewJvTP2XMuKvL93lIdqJ1xWTbdeyXYzlhdS0VffnXyvI2st2K024/ry/Vtjmf89Ubj7Mq3UDvSG25svLxrCXsXbMIky6AW6bex5gralkC4SKjKEp1VmMbDU3nMdvNBaEKDKTZJ59gOXiQnK++IuPFl8DpxHdy0yji5eUXSrIE9oQjDS1KrRjZ/yUO5Oznx6IDvC47EZug6b0+yMt3JazKKTITXY32aWm/43QGEh529srDKj8/WkY/ywHH6+yePYluN6+oE1lLMl31f44V5bLr80/odO8D1e4rCALBscGUlK3wyE4Hucnb+XPaDq5652ecAiQ67KgkkU9ffqm8X3dS+HfrJhZa/6Sr0pdXJj1dJ8cC8MUjK+n/ZTtme3iwU9nNI8eXExs77PwdGynjHniD4J+nsfiYk2/+XMKd4wdTnHmSNsNvc4WxKwo2swlHaR7vfvotAD+8+SgaQSbNoqEEI6GqIu5+/gNyEjaxe91i1FojarUKz4AwLHkpzN9fDHjSxrOUQZNdf6ev5/xL3sGNSEEteGPq9WibUAp/2W1hcdOY0bVpQ8T06Rj79qV4WdPJ1Cip1KTFehOUVHz+xo2UtkEdkQWB4sLEhhal0RDk7Vrr9/f1OW/b7OwE1OpDeHtfhXQevw2fNoMQbAIS1U/Adj7ib7uTR376E4A1KxYi19BCedUjtxDdeSwTn5vOQz9+i3dIBwRRz5JtaQBoFdcTryPpYHmftL2LSXzzU/rtlfnypg/r7FhOMVE/kAiTkdUama3H5tf5+BebXje/wPV9YnAisWzen7QZMaXcSUMQBLQGI8bAZrzwzNN0D1eh1mhBpSHU03U+pTu82D7zTT75bTFrk2HT0RxWHszmz7UHmb+/GDV2Hrv7Zq57/F3UZTWVTpw8iUnQ88K9NzQpZQVAQWlKBha3heVyRd+5E/k//9LQYtQIe5Av3ocLWfXtNAbe8UJDi1Njmvm1hmOQnL4db9+mm2yqLtGILhO8VI1Mxnv2foYsq+jc+fx+KYlr3kHxVfD1qH6K++qQvmE9AN269KxxBl1Rkpj4P5fsVrOVwow9+EV0p+TUMoQgIAgC1oBotDkny/sV+1/FnUt2oX2vcm6XC+XhWz4FoPf37XgnbQXbf+nPuzetqfN5Liath99Kt733s604kAPLf6Xt0BsrtZG0eq68y1UkM//Ebj76cXb5vnmHrERKWUy84zG8Q2NBkck/vhOHykBgdNtKY7VoHk3SjkT+Xr2DSYO61d+B1QOy3LQsLG6F5TJF5eeHs6AAW2IimqimEWo7+INfWX3nOAI/+Y19bTsT33tMQ4tUIyLDuiGLHryTsJfe2XDQ7MQggqjIgIJTAacCDsCJgBMBBwJORBxlrxVAREFQFNdvXGZSAQUBpew1Z+xz3QxFKrY/9Vo4o91/+wqAKJTtV06/rjg+p+cVyl4rIApUkOdU2yT1EUKFvQxyCAgImE1moqJ82HMikYTM3xFFCUGQEAUVgiAhlL13RdfMRxTVpKV+7krahkhxyUGczhI8PdsjIIAgIiDgjNVBHhzz/pH8mTuQHRa06kDaXfvTBSUSc5RFpIS071TrMQD2rdoOgE9wBKEqVzRLb6EAgCemT8diszP92XX4m00IkhcqRY3D6UBVjWiq2vBOm7uYeXgWm205JCdvpFmz3vUyz8Xiykc+Iu2tp/ln3T5C4/biG9X+rG3VBm9CVYU4FAFRkWkdGciAmz5CPFWYVJDwbXF2RWTK2IE8vWsrO3bva3IKC7i+q00Ft8JymeI1dixZH0yncN58Ah+4v6HFqRZGLz96fPgDe2+YiObOJ9n/o5p23ZqOE6unTxTNPIew1PtqNtpKcagkzJKOdtZUFAQkFCRBQYWMSlGQkFEhI6GgQkGDjAAoCMiCgCKIOBWXEiMLAiiu3/KpNmW/lTJFR0ZAPvO1AJRtO3O7cuYcVe0Xzmxbtr9MCXC1FctlksvUlVNtnHRDYTKHVL8w0bYAdBAWkoMiSjidMk6ngiCUSS6UzSK4tgkCCIKd5JTKUWJFRbvO+rnnBe0te3WEFnlJ6PxrrqDLsoIoCgR27oLaKXNw4Vyajx1X43FO0a5vJ1b9AHarnSX/zITgEfRs7Y3slCkqdfLNF7sJsKlA8iKgYC9qh5nZz93ANW/9Xus5z0W/Xo9QbM1nzcm/GbnqUR5q/ii3dB2DXnf+Gk6NEUFSce3km/nwpzl89/1PjB/el9g+Vf+9PIKjufv56bWfSxDwDoqgICMRu8OJWtV0/NNkRWlScc1uheUyRfLwQNe6NdajRxtalBoREBaL5tWn0Ux9FUdeQUOLU2OiI65gV5GdI8OuOGNrp4YS56Jjd9p5YusMZphv5YBxCK/EhTI8uEu1rB42WxGKYkNRHCiKA1lxkp21hGPH3yY8bDIxMQ+iKAqK4gRkFEXGaTWRvPQ9sjRrUDSg8a1Z9tEVm2fz3fo/2WmNZVRpc+6Z1BsvtRar+cKKrxxYvw+A5P1LWBx9J4FOgeKtOj7d+C8yoAc0Pgq2ApGet/TEvOsLPA8knXPMC2VUv5fIKk7lBWkoL9la8dLGIxzv1QKDvmmWk/Bp3olJnVaxbFcRPy/dSavNm+ndqxfR9ZEcT63FiJXcohKcikiYn0e9lASoSxRFocTiwKBpOgqW2+n2MkYVFIQzL6+hxagx6lIrAHrDhWUvbQga9yWs/lFLaj7seQufR+WS4jAy7qBEz9WLmbZ7Lukl6ZXayyYTisOVq0Wj8UKrDUCnC0Gvj8BoiCI6eiqS5ElG5j9oNEFl+8LR65thMETh6duG6EHP4vSW8SyKrFF0lsNiYcfcDcQVxTGEfOZ7JDBs5hGsPXoROVBkxcpYVqyMZc2Sm8oKIVafrJPHACiUdIiyk0FmNU5Fhjg16hYaul8djFfKdrTODCKGdCS9dSA+GaU1mqOmCKJIs66PY9e2LN+24/iuep3zQpFtxRxbMZ6di7pQlLyo0v748Y8w9f5H8RAtnCxU+GHJDrbMfLdOZZi5cgf25L3gG8HSDbv46uP3efLN/yMxI/f8nRuQApOdYquDKL+mkeQO3ArL5U0TMweeok3fsZTqBJLff7uhRakxwhn/X64IgsCE5kPY3r8fn8VYiNWY+SovhB5bkrlr46/I8ulcO8fHXsWh+PYU/PnnWZWCyGa34nSaSE2bUeV+Q1AsnqlhFBuTKDi6rloyWgoL+fHVVylVu5ZE/K3+tAhZzrfDH6ZFx68oCtpR3tau3sjSmQ+x99MHSf3rtWqNP/T2SWRr/FkeOJZH821EOSS8nCLiEQfKUTvHd8zGf8rXxEx+gdXr49F1Tyejx7nqVNcNizNTQRD5LcKlJE5NcTB80dxGmbPJlrOXbcu7c1LYS562kG0J92PO3VOpnTGwGY8+9zpPPz8NP6GIhYdKKUrcW8WItWPvLpc/Uou2HTi+7V8EAdS2Yj7/7ucaR5JdTL5ccxwAndptYXHTBHCWFCPqm16ZeE9Pf3KmjCLkYBYZJw80tDg1Q7iwpYRLCa2k4uroXvzW52q29YxjpEc68yztKLCdDl23p7rKSaQ//wKpDz1E5ltvc2zESAoXLChvEx39IIKg5sSJT846V/yQr5EsKrYfvZWdv43DUVJw1raKovDW9Okka05HLh0K0DLJ4/SF3WTyqdDH6rGeBRn+hO+t3tN7cm4x/4SM4ZrsDYiSX/n20OBcWvdNJ7THD4jHQ2nOHXinxWDuLVPQp/5raQ0KdCXmeyk1mVvlY3SRs9mji2T3iZ3kF+fU+/zVIWfLy+xa0oMNO8djkex0i3yd/l2WIMlwYNP1mJKWVuojSRKiSkWrUJdV1iOicrRPbfjyr+Woi1zn6NH1C8mX9Vx90x2Et+mKzlbAM+98RonZWidz1SWKovDFapeVr3lg0/FTcisslzGCSo09M7PG5uzGQNurbgFg82evNLAkNWO/xQiCius3zWPypnmszTre0CI1CoINvvT1cflKSILrsqTYbBxqOZkjLa8hs8VQju7KI2HeDrLzBEr+XVXeVxRV+PsPwmbLIi3tzyrHN4S1psegJQQV9CTPfx+b5g/AVlK1yb6qp+K2mZC37iESv38F+ZXuBH2RT/KKrhw6dAUHDvRnz+7hdBF2AWBKP79fmFqEe5J/o5Wnq7yACgcjvVT0sIaQa1jKHfzM1NjXuU3qwITwN/iER1DH9TvvuBfK6PA29NYmc1iJYZmo5aZQlzI1MkniuvWb633+83Fs5UR2l/yM2ZlPqNKcbh1+wLvF9ah9WhAf9ghFGgu79t1D0f5vMaX8iyWnosUlp6CEOEMx4nny+FQXi/V0HSNZEZgwYQIdWjTjvmuG067vSPSWHD788a86masueX2BK9fPq+PaEeXfdBQWt9PtZYzXlaNJe/wJrAkJ6Fq3bmhxakRkTEcWD2xJi3l7SLtnL2HNzx622JjwUOnBBmtLvHFKXpQc3ku/IHdOFiiLWAByLEV4a71wWm2khZ2RLv4Mf9nw1hkV+losrqfchMMvERZWdb0mXWAk7a+fQeaW39nvfIY9f15DTPenObHtbVr2fwOvGFd9IEmSeOGFF1g3cBCKILCx9zV4FrTBZFnAs7ffAsTz23MP0mthCrt79uNgiJ6g4izCsreREK3BuHEW1pH3smLXXNpHdKFVZIdKskQE+9EitC8dtN2JkxUUwYolrAgh3Jdjxddj8TXgKxRjVkTsaNgo9COnUEvljCJ1z+w+Y+m8cjYlso4RHQaxJjWB7w7s4AdtO55Z/hftjGpuaoCqzhlbniVR3kmEEkfLkQsrFWr0j3+IDg47u3M+ZWvmG5AJRotEr9GHAbCbijhu0jMgpu6syg9eP5IDJzsQEeCLt6eh3NFWFEW6t49j/7rFWG2Vq243FHanTKHZztIDrgrWQ9oEN7BENcOtsFzGqAIDAVDsjgaWpHa0uP8J7KumcnTt/CajsCzo2aP8dfTy5XD+mn+XDYayvBcjd5zkyKAIRKPryW/wLa1p1TMEh11m++JEdixOpLjNwP/0dllFOnf6+bzzBPe4lpJFuzkZOZNdmfdBM9h64kbCtg+hzaSvAJfSEpSXB04nsTnJZGBAtJwuC/HdlGd585gHVwCdEqfj3HgQAU9+NnizIH8Wjj9mAhC624vFd6+tkGTOZjJz7Je1tFbFAxB4dwt8YsPK96+cv5P2JoWlo/siCAJ22U6z1fvRak+3qU9+PbGddJoRrz6OIIq0bNaGW2Un6w8m848YyM8mAzddFEkqcjh/Jv4OT1oM++OsVaX9Oz3OFbkjydj7Fsec67GJp5fRLIVZOFERGFF10czaIIoi8c2rjjzLyC4AICL84vzdqsPUn7bxb4KrDtcDg1oQ7tO0XALcS0KXMfqOHRE0Gsw7dza0KLUiNr4v6cEabN//xpGd/za0ODWmqZV2r28mxfRlqPYYxXiwPGUbssOlhKz86RC/v7mN317ZzI7FrrIG//58CIf9tHOuIEgIghofny7VmitmxDRCcioW+0vzW4ElP7X8vejp8ndov34uoioSURXF05tc6/46fUB5u9iIXcR+/zb5ngKhijfXBo7hPr8buEV7JemGIv5Zd1qJyt5zjKPTluF5XI9FZ8b/8Q4VlBWAEhG6aHTlT+tqUY3WmUWBo/4Lf+7JT+PZo1l4yDn80mN4+fY2UfGsHTmK5zwLcYgqhiyaz2NLZtW7POUoCrKg4OvbG0njdc6mWv92RA38mVb6Mdg1IoVJ8wAQta6/p8NSjNlc/yU+erSNoVQwcCzhIPaL8LerDplFVga2CuSLm7pyZ7/qKW6ybCUt7XeSk3/AbK7f0Prz4VZYLmNErRZtm9bk/fwzlkOHGlqcGiMIAo7briY4y45j8n0c2Ljg/J0aEa5kcW5OIYkS0+L7APDO8TT+t3s+kkYkLM6HsFhvotoH0LZvGBOf6spN03qhOiO6QRAkoPq+WKIo0u7aH4m2VlxkUXsGlr+OW7uGVnv3ELdxA3HKIRRHMoZ9c1i2soQX91nwkP4mSDsZlZQLc+7DaFVo4RXDM1e9wb1jn+Hxa98gxOLF1/u/wVJYzJ73ZmP+NRnRKSIPN9Ju2jj0gZUjfzxkKFEq+tG01JpJIQqLo/6soT+f2MHIHSdxikZ+6NSeEL1PpTaD4zpxtyMBPQ5mqVvUmyyVKP88qn/LCmhxK4KisO3oI+xe0JnCE7Po3GU+aerprN/QicWL78JmM9WPvJSdY916Y7Dls2FP48h35ZQVov2NjIwPwcdw/nIYAAWFOzh46BkOH5lGYtI39SzhuXEvCV3mhL//Acl33cWJCVfjd8stBD/zv4YWqUYMn/ISm7wD8X72/zj+8Tu07X1lQ4tUbRQkNtqiaL58MXeFSDwT33Qr5dYVMV6hDNBu5LjNwC8lIbzzsI0JsdWxmki1ch6PGfESLJFJMf2Jw2iHM0KqRbVriUr09aXDrR3Z9/FiQgM8KL06jPtnJeBwduIjzRa6K/vQ6ouJH1NCZn4pTouDgoJccnIzmaS5ClNOCcmvr8dH8KUk3ETcHUNRG89elNEpgMVZUWEptNtAgK+ObeGhVn1qfJznI8tcwpMnRRANrO8eQYxHQJXtQgOa8cqw6/jw3z/YhkR2QRaBPkF1Lk8lZKfLHFmDPDraoK70avcLKXteJll/jJzCj/DQC0hCf+yKjFqzkrXr2mO1diAocCjdulXM+C3LTtaseQa7veA8M53dTKo3FREdU8reY+mkZhVhcaiRKEItmsr161NnbdWnr/AfK6xQ+VWFBlXtBxQBBLg6yk6QXse27Vpk2YYinxF5Vj7O6d8CAg6nyxql1YaUJWVsONwKy2WOJiKc5nP/IeeLL8n59FO8xlyJvn3T8Ac5Rfrif/AGOrz0XkOLUiNuCLSzo/Ak++VgVucm8kxDC9RImNXnamz2IiLXHWdjTiLjw+Pw0vlW2dbmtLI4ZQdJtnA8qXnYrShKxI56Da/dPdiT+ygnlk6jxZg3KrVL3LMLgCMZyaS/dw9DFW9mh4zjUWEq90j/cJNqBZJGId77dtJf3giAEbiSPhRKxWQH5tP26gFExp7fn8GAQJpc8cYgCgKCYuO25l1rfIzV4dm9K4Ao7g7IJsaj03nb9w0Ohky4fuMW2omlfDTsGoQaFoOsCabMjTglAdlZM4uIIaQPzT1/xLBrGnrRF58ODyEZXY6mhxL+5MSJX1CUdAqLPmDNmjT693+9vG96+g6c8l8giNjtPmeZ4dxKsqeHgkEv4yFmIQIeGicFjjisSiAggnBKPVAQhNPKiXKGNqOc8aL89an/lVMblVO/KrQ51QTBtUNUga+XBwa9HkHUIIpqTisoyn9+nR5Do/EnJ2fFOY/1YuBWWNwgqNUE3HsPRUsWk/X+B0R+/12jTyt9JmE7UjgyvDVXte7e0KLUiLc7uiwqLVauxNb0IsvrFbXKkygxm9mmOFod3cgj8aOrbDf7+BoeTgkEbsNICbfWYi7ZYaPo+HowQmlx1Uujfq1aw6olhClFBBpLGDBkBDe0jWPUrxms0A3n+ZI7eChuNdcecSlWyaOtePv5ExAaShu/IEShejfzuTtS2KFXeFFX0U8jXzagiBoK7Sa81HVTtdnudPDjiW1syMtlcaERSSjklfbVs/J1aN6FBzIWsR0Vv2taMa00H4POE3U1qm7XhuTtT4IHeHhXjrg6HypjKBFXfFZpe+tWk2jdahKKorBw0Y2gm8nChZvo2/dXvLxCyMpyhUS3avkTkZFNuxhkXZCTs5KaLLvWB26FxQ0AgkpF0GOPk3LffZRu2IDHFVecv1MjoSDMC9/txyjMSsE7qGa1YhoDKhzYlKajIF4MBEFgU/+htFm1mhk5EuvXz0YUXB4MUtlTqSRAss11CbvZ8wh/FDer8TyKonB86Yskev+JNtuT2D4vVtmu5eBhPDZgMPZnQ3HoW2C47nEA+nl+QYldwAMzenVHbN1T0GyNoNXWFHwfGoRQgyyiqbmlPJybzSCbyO39oivsG+QtMKcYvj22nZfbD63xcZ5Jsc3MrduWsdESgiLoQAklVJXGi7GB5+9chkbnwfODr2Hu9iVsLIJW25IJtuWxa9jAOre0OG1FFJAJqPBvc2+djg2uc23Y0B9Y+e8TqNRLWLN2EpIUgCQdwm4PJzy8x/kHuQwQGkGGbrfTrZtyPAYNRPLzw7x9e0OLUiMiXngRjdnOpkdva2hRaoUKGbtbYamEIAhc65NPsGSmVBYodArkOQQy7CJpdolEq4RDgYHaE3ipJESqlwbd6bCSuvJLjsx5gjV/tyFR9weaTD19r9uFR1Snyu1zM7BtXYTj+VA0eitizynl+0a08mWLJYIS9CTlm2k+cTK+vQsxZUeT+8EsFGv1s9PuOJaLWSXwVq8WaP6j6HzUeTiSXMJPmQ5WpB8u355ZXEh2SVG151iVlUL3tavYYImgoyqRtyLtJA/syM5BY5gQ2bHa45xiWLu+fOmZyc32BDI1fiSl1b3z/t6VQyjxUNE56HEEqX4sOBqNhpEjPqZZxLvo9eloNHuxWFrRq+f3SHWUZO6SoIGTjLotLG7KEQQBxWpFaSQheNWlXfdRzO31GXErj6IoSpNazgJQCzI22f3sUBWvdp5QrXav7Jx9XoVFUVyr8kfmP0ei11z2eHXGktyP6zW+hA+6s0Jb247lKLNuRRKtqLR2VzSXFkymcPTDTysswwb05+ltrrpCt4xwWSWN48YgqueSuyaInHf/xv+RsYge5y8wl2SzoZIVgnwqO+RqJYknI0TeT9Zx8/o8fFL/pr2Pic2HjIiSnd/v6Uq3iNizjr095yTPHdrHLnsEGlngjViF25tfc16ZzodeZ2RctxF47l3NzznQ84iNFY49tIuu+dJNJRSFQ8tHkKvJw8/qiV/8fRc+5nlo0+YqdLog1GqRiAi3ZaUCjeC66lZY3FRAFx9P4fx5BD32aEOLUiMMMbHIwlFMJfkYPf3O36ERoRZkzIpbYbkQnGUG6+OFyQAsTt3FtMxwBBQUBJQzjcneN8Gp1GeRoM1K5KmQlhXGcxxcj0FfQqnUA6vGE8UjFM3gWzG0qXgT8wsM5ZbQFNqEetGmdZvy7frRVxHosYSchd5kvzefwEdHIHqfu3jhGpOZzmYF7Vme6G+M7EpJThJfrz6Cya5lc7oWb+9cioq8eOiPVWx4tGqF5bfEvTx63AlE0Fk8xC/9RuKv8zmnLDVlcPsBvLFhHs9amzHkhMykQzO5OSaanq161XrMtPX3kyodI8jiR9vBlesD1RcxMbWX2U394lZY3FTAZ+LVpD31NM6SUiSPplNjwtiyNaKyhMKMpCansGgEhWJ3RpYL4oDNh2I86LPjVH0glz/LUwGJgIIoKAiAoCiIAljyU9maFsKqoDi+8gjgqbOMq7v7O6Sgc/vGvPrw3VVu1/YfQaBuLdl/K+R8sJCAR0ci+lQd7WRxymwWHdxfhUOtoig8/W8Cv688Dg4FUSvy/X09iTDaCDH60uXtWZgsVV/Kvzy6jZeSXfsmeWfzSZfrz3ksF8LtfcYibV7E3/l2FqiiMSYm1kphcZSms2/1aHJ1RYQ4I2k7ckW9RiC5qS5uC4ubRoagc5mjFZsVV2Bm08BTcsmak3mSsLhODStMDVELCg63wnJBONShCJj4MsZcvq25ZxDx/uOqbP/FvKWsCgqiRU4Gfezmyg3kuknQpunRj0DVFrJ/F8j5cDEBj1+J6Fk5U+vG1AIsksDg0IoKTYHFzrift5B4rIDw5j68MaoNfcJ8UEuuG/jqoyewmQMY2amyH4uiKLyUrEJSrHTSpPF4XP86OaZzcWvPUdwKDFk0H6kGdxdFlpn5z5co1jVE+WzBooNmjhbEDVnoVlbclONWWNxUQCmrPiqom1aRm2xHPiFAcGDd1Qm5WKgFAdntdHtByIqIiMBV0dVLqjbDZKVX1j7m3OFaGlIUBdv2ZTjW/oyUsR69NhdZFhD0Hhcsm6ZLDwLlTWT/KZDzwQICHh+L6FFx3MnHXCnPO8adTtiWVmpl6JfrKc2xcO3wFrw7uFWlsXtHRyGpdnAyp3J+kkOF6QDcFmjl9fbV8wWqK2QEspwC249so3V4S4yGs6fTL8zL5pmv/mJhQSTtAzrzmvdhOrZ4HY/IkRdRYjfnp+GvUW6FxU2VyMXFSGW1VBoC8w+PIh2aiaPZCPT3focgiihWM5Zl36FqOxB183blbRWng3arvsL3qgy2Lb6J1LwpBEUNICQoHpWqbnJW1CcqQcDpDtirNVP2HmdnsalGPoHFXjrsHgKJ712Lf0EGGtshtAYraqeAVQjFFHYTmuH3oPasegmnpmi69SLAuY5tC9MZ8ldvFo6YQ7OQ0z4nowUNCxUbhwpNxHtoyChTVky5Ft64oRM3xledcE6jEokOKeFwqlTJ4fylgztBDuCOmPg6OYaa4IGd+dqWzE+BoBPbec7bRP/YDoQGVFxe27ppDY/MTaJY8Qegd2woHUc1rSjFywmlgfOwuK+Sbipg6NIZgOKVDVtMUDm0DI3OhCF7NtZnoil9cyzyq2Hotz2L9N0VmH94FNOX92B6rgv2Z5oR6J1MjiqIaFM2XZa8QsRXg9n9x+QGPYbq0kwnYZECSSwtbGhRmiTLc4uRFYUrfKqvYL8fruGEIYIZAc3QyQdxeLbB3PEleOo4+rcOYrznU9R1XAFc27MvGb3SABi9ZDwb9qwu3/dWpxhiTQrjDpzgUFoBQ8qUlbfOoaycYlR8BBZTIMuP7ivflmbKZ63Jn67aNJp7XHyfrhn9BrI6VsMVluNkqX152BTOgJ2JZGe7ilc6bDamf/0d180pJFxr4uvrowBoGVJ1SQA3DU/D21fcCoub/2De48ruKJ0noqG+ka79PxQnWBzNELFhMK3B4ozB1PFVbFZv9Ce/w5D+G5ItFafggaXH24S+kUDo/9I5OuFT8iQV3kmbG/QYqssjLeIBgc+P7W5oUZos7T0NzOp09rDeMzGZivggJQ8ArX8sqtcyMbywGv2ExxDr2WF7wvDT7r0P7HiEuStmocgKQT56fusZR6kIw37dRmmZZeX6dudP5X9P776Ikonp/24q3/Z30g4UUcdTrRomNNfTw4dWkW35c8R4Uvq14xZ7AkUqD97csZEjR/Zy/Rs/8n/HAnm4RTYznrmNfMUCQJvw6AaR103TwL0k5KYC5r37UAUF4T12TIPKoe0yiNLvjIg6M+p301FKSzGWrfvLA67DvP4vRN8gtH3/k0tCEGjR8SYS1n9IdPYRcnMO4R/QugGOoPq09o4AMihxVOH86abOmbdvHdv0MXzlk8uY/nfDRXTqFCUVnrLCFO+O7C/15LmU11jw3UIeu+IJWrVpT9zBYpLzrLx6Q0duqIayAuCh1dI9TmHLEW8yi/MI9vRjaZ7LabinX2h9Hs55EUQRlSjyzvDr2LxoAb+ZY5j3/XGC0DNrvDfde40F4HB6BpKgo0Vo8waV183ZaQzVQ9wWFjcV0MbF4cjOxp6R0aByWJb9hDGgFMVuRxCECk6Kol8I+rH3V1ZWzkDV+z60Cvh/0hOrtXEvtVidVhDU6KWm5ejcVCmx2xEVJ2M6DERsgCymEgKiYmP6TZ/zWuuXOCYlce3mm3jh5yeIzDXRSafllvY1KzHx9NA+KE4905YtA2CL1VXg75vju2pVxbouyTXb2ZiWz0utu+B9ogCtXmThEyPo3ut01NKh9FLCPfPR1lGdJDf1gOJEqGZNrPrCbWFxUwGPgQOQvLxIe+YZor7/vkFkUGQZ3foHARBGvlyrMWI7TeHkimlEl+SxY8YEek9ZWYcS1i3FNpdlZb/Fg3dOpCMiIAkgIuBEId1qx6EoGEQRT5WEURIxOWVMThmHouAE+vt6MDrQp0GP42IzeMshEi027IpSI2fAdKeIpMg4HTZESV+PElaNKAg4HVYEQWBcz0mM6DKGn5Z/zbdps8g0DwJqnmm6S0QEESHLWLpbi3O8wuJOQUzccYDXUgIosa3if+0G1f2BVJOBn6+jOOt0FNMVPSLw9qtYt+h4HsT42S62aG5qgIITQWjY9AtuhcVNBVS+vvhcey2533+Ps6gIyatiOOLCmW+wPHMNgkMmOqglD978SZ3LYD+yGw1gN6nQj7q9doMIAtFPnODke81pldK4fUNsTjNqyyGSVC2ZmZ6HrIATBVlxObqFatWoRQGTU6bE6aTUIaOXRIySiEoQSLfa2VdsuuwUlgOlLr+HaL2GKeHVK9xXmrKTny0+NLclo1J3rU/xzooKEafTWv5ep9YxddSDTCqcTJc3XX5XW5OP0r1ZixqNO6FLMP+3UGFPWhqdI8J5Ny6L+46BU2hYq4Xd7sQ3zIN7+scgCgLXtgqp1Cal0IPe0ZVDs900HhRFRmjgfFFuhcVNJbwnjCf/11/J+eJLgp96snz77AXv86L1N/Ap2yCnEzDnNSaPf75O51fHueqQOJwGLniRxGkl1TOAxpz71uq04JP1Oj91/YnOQe3O3+E/PHQwkZPmy+/pVAAG+Hkys2P1nG0BBK0nRSqFQrUni3atYHSXYbWa2+l00nX5KiyihgOD+9RoaUkSBJxK5bpHft4BrP9fHwZ+sIQpP61l85MRGDWV6wqdjZGt2vN/C/fw195tdI4I56vERAQ5iIfiulV7jLqg0GLnlXVHySq2smFfJnKpg9BAI3d3iqyyfWZBFiV2A3HBF9/a5ab6KIqzwesJuX1Y3FRC27w52nGjWb76e/JSjpevgf/fyZ8AmGLpxhtBdwHwRuEsNq+eWafzC6KEtUiDSmNGkatXgfdsFPjHEleYicPeeJ/eTA6XbHpV7S7YsnL5fpFr6p7h0PujlK3DO2tb+VdReHvZz2Ro/ClQedL83210XbKcNTuXoNit5+0u4rKgVUW4jy9vXRNHSbE/N/0yq0ZitQuKYKpmFpO3PMn2z7owede7BNtLEIWL58NSYnPQ6//W8Pfy46zbnAoKeIUYeGNk1Y7vGfmZ9HnbFd3UNiL6osnppuYoiuxeEnLTOFnZXcv0aIlf/roaq0bEhoNCD9eFTzIYGTvqIXonDGDo+hu58+TrhB54j7YlXoRoArDi4IUH/0S8gOgLZ0hPtKa12HauQNO1dk/BAIZud6BLeYht69+h28CXaz1OfVJiLwHAqKpdKQQZl1/E5UhNE1kZ9J74OIoZoCpmbMfRtZpzzppf+VjbiRf0mXTx8WZXTibzi+Hagih8Vm3hIY8S7ug+DK2q6surqsw36WxMbN+VZV2Ps3h7AB+tXcHD/YaU78sptnA0u5Rezf+/vfuOj6LMHzj+mdm+m2TTC5BAQihCCB0MoCgIyCEqNrARK4qH9TzF36nAWe/0VOyNQxRUwHKKIogU6SVIgNAJCem9l012d+b3x4aEmAAJLEkIz/v1yiubmdmZZ/ZJst99yvfxa/C8nYtf5P/kH9ht6MDAnEQGArH5N/GF9W3ujIk9q3ttrjXH86nMtyGbNOx6ejRW46nbSN/46WvmbdPgVM1MHZBP3y5nVx9CS3EitfJHIxGwCI3aZchGI2mokp3kml2DAMfldWBo8BCum+zqAvLv0ZfffJfz+qKHWGM5zurAXCAXgO8/i8apkRiY50VHjR+enn54SkYUVWVv5VGc1VWUYsNP9eDB0c/Qt99YAJTyMiSjCc3Am2HDBpxLH4WB+8/6Prr3ncqBNS/Q+/e3SNBZiIi+g8NxHxLQdSwdw4af24vkJjkVOQAEmJs2DuPPnKqKph3EKykJu9m0ZBF+nUJdwa4kI8sykiwjSRIarZa+Yyfg5V/3OjW37UCr0TK56iDf0hOHU0FbsyZPeWUpu5L2UGGvxqkqOBTXgGaHoiKhoCoKTlUlrSCDd/XR3CSn8dDQCUiSRAwwpbyUdYm72Zp+nJcqezJ/9TrGSblcZAF4NwAANJtJREFUEdyB0VGX1VsPR8OpW1hOeP/GG7n0+ALmrvDgiq4p9O3g6k4Z+9YGCsqrMek0RPtDVM4ypt4wkaDeI4k4+BGbrBMZ9tgXIEmkvB1DWMF+RkWOaOardO7+PaXfaYOVD35+hbc3RjOwQw4v39ifHh2bN15HaHmqKgbdCm1Qka2IjRkbeXLQk8SoEbz52yxu6XMHl8c2/JTmHxDGq4/95FqLpbwUg4cXC1a+ysq4r0no6KREZ2enNQlIQlJUVI2Et05DJ9UDk9aLY/Z87t/xN740fUKnrOMYVk9DVUGye4IRdHK2q93/bFsQJIkuD2zmyKcjiV71Is5VL9IPqP79LTaG9WPIbT+gN3o3+XQJeQk8se4JQiwhvDf6PTz0zVtrJqcih3d3vYtG1mDWmjFqjezL34fVYMWobfp4hZM5VRVNm8hDeW6+e3U2TrsdxWFHVVWuUG8E4HjZfg4UbaHYnsfO5T8wY/4StDodEs3vEgK4JTyCj3I8CV+3E7uso2tVFqk6X6plC2dc8NPUiYjKdF4bM65eGnwfiyeTokcwKXoE9xzezAfH8viFQD7NtzJ45Y8817UTQ7oPAlVFdTqxyacueJW9nOS8LTw2Yj+JicdYsuYHLpnyGXqtllKbHY0s4WXSsi2zim38hfE/PEenH49glFQ6j7qntly5fn2QKwro6O+e3CYOp8L2jGJyKqupdDjRyDI6WcKgkbkizBezTkNOpb1J51pz2JUn5osHpmA2tM2xK6qqUl2dg8EQ1NpFaRNUVYELKWD54IMP+OCDD0hOTgagd+/ePP/884wfPx6AxMREnnzySTZu3EhVVRVXX30177zzDkFBp67w2bNnM2fOnHrbevTowcGDB5t5K4K7/Hr8V1RV5erwq/E3+fNu71VnfI4kSRg8XDOKYsfNJHbczNp9O/f+SmTEIMwaI6qqojfVvSkUZiQx+ZtJ3LTpPpwaiY2yhM44HLUoDT2lVKshaM+xu8NkCaTnA1vZ8csjyKXZ6MJHErX6VUakxMOrncl7aDP+Zxjsqqoq8bnxTP1lKgCZ5ZlM+nESq26qe23sip3DBYfRyBr0Gj16WY9BY0CvqfsenxPP90e/p4dPD6qcVdicNqocVQwNHnrW96ecQzzXlkRdOZbdv/7M7S+/CUDazA0ABJvD6ezRi90F6zhYvI3dvy5n4ATXKsxnMzqjd+/RfGhfxw9ZeazSdcEm63nWkM7lYT3w8/BGI8toZS0aWUaj0aCioUSB4TuPUeZUeHfwYEynWRy0R/dhvNXdtQjj+oT1vJCq4dp0Lb2OLceuQq4xFIeu8RT0O459Tkriy3hr7AQBsr+JAF0lSza8zeXRD2B3qlwa4cvX02I4lpLGqPd3k2qIROo4Emvfa4nsW5fbpNojhE62LMqrKrEYzU16bRRF4f1dqeRV2nFK4G8x8Gg/1/o/035KYM2W1Eafp/fUc9WADiz/PRkAn9O0rgCM7xvFjl9h6ZbfiL1iYpPK1lIUxUFm1rdkZCyhpCQegyEESZIxmyPwtg6k2l6Iqjqx2dLIz19HVNS7BAWOb+1in3+qcmHlYenUqROvvvoq3bp1Q1VVFixYwHXXXceuXbvo0qULY8eOpW/fvqxZ48p58dxzzzFx4kS2bt162vEMvXv35rfffqsr1Cn6foWWsSxxGTEdYvA3uWddj4F9xp5yn0+HcD64fC7X754BgP2+tVg7udYzchbkYvLydksZ9EYrgyctqP1ZHf40/NO1sN3BXx5nROyvDZ6TWJTIgn0L2Ji+kcKqQhyKg2j/aO7rcx+vbH+FzPJM+izow6MDHqWLVxcWH1rM1sytDc5zMrnmD/6zqz9rduvMqThR0bSDiMXT1w+jxYOCjDQ0Wh1FjlxkvQavPh1hbyXh3Qag9/Khc3S/2uec7WJs1/e7guubcfzorfspcyo83jmIAQGBTX7e5VGXs/ISJ8t2rWJTZT42WcuvkkylqeG8te/XDcNLycYkSeQah6CgQVb2AZVYjqQxeaVr7aHRHV2/t2aja7pynv9QxnfuRPXmZSgRfZCtPhx+4QFCN6zk9xEjGNnEYAVgTUohry9NqLdt6fYUvIw6jmWUgF7mtTv6Y9FqcCiuLrMnv9xFdWl1bbByz7U9GRV2+nl5Y/oO5Z+/7mLWCpn1Rz7l/bvubBNJ41RVZdeuOygq3oGPTwwdQm5BknU4HCXk5q6itDQBvd4fh6MMDw/X6tkJCTPI8L2MPlHvotW652+6LVK5wKY1T5xYPxJ+6aWX+OCDD9i6dSvp6ekkJyeza9cuvGpydyxYsAAfHx/WrFnDVVdddepCaLUEBzecmy+0vJ3ZO4nPjef1ka+3zAVVlUfjHgCdjqv1gfjXBCsAGt+zG9PRFJIsc8RooZutnBFJ2yiuzMdqcg1kTC5O5vnNz7MrZxf+Jn/Gh4/HqDFi1pm5ufvNWA1WRoaO5JM9n/Bu/LvM/WNu7XktOgvzxs6jyllFlbMKu2Kve+x0PfY2eLstWAHQSRKVzuYnG2tr9CYTtvIy5j/+IACBxjCuDLkV9roS63nle9Ex+hqsoZ1rn9MS81+OV1ZxrLKa4d4ePB3R/FT3skbDdYOu5rqan4cs/aZBB15B8V68lGwADLJKgG07AGk2Cx0OP4xPTh86XaIlKcSTRds3osnaRW7iYa7KKUG7N55fKmz0zCzAsHgF5q4hOLekUoqJsl0mjh48RGTPHk0qa3G1q6tm9m19seq0PLl0N5nZZWRpZCRZIjLSh5u71/9ffe1zY8m12VFVMGhk/ExnTkYQ6teBz6em8cTSo6xODOFI5lGiwpo/pd+dbFVZHDnyMkXFO4gIf5zw8Bn19iuKA1mu/5bpcJSTmjqfY0lvkpT8Dt0in2nJIrco17TmC6iF5WROp5OlS5dSXl5OTEwMiYmJrm4BQ12UbDQakWWZjRs3njZgOXLkCB06dMBoNBITE8Mrr7xCWFjjc/YBqqqqqKqqmz5YUlJytrchnGRP7h4eWfMIffz7MKbz2c/MaRZJoqSm9e3JK15rmWvWKBlwG2z+BIDEX2di8YtE6xPBi4cXsaskkReHv8iYzmMw6xp+QpUlmQf6PsCUnlOYlzCP1cdX8+WELzFrzehaOMV+F5OBpVkFqKpab1zFhabvmL8QEtkDR3U1TocDRXGi2SPh3F+OoZs3VUeKcJb8Kd9MC0QsuprX1OmmFPeSpEFR6weYvtY+mAMn49CHYTYE4mXuQCdrL/bMXc/vpUcpNGxlQ9gEAA4OH8Rvm34mMiOZIIedAg8TBR4mBo0fgbpuNWV7jmMO82DP1bFEfvwBCS++QuTCz5pUtnnbXKspB1gMTOgawKTnz/xBUquRCbE0v3Xk8l5DWHCPLxPeO0RWYU6rBiyVlals2z4BkOga8SRdukxvcMyfgxUArdZCePgM8vLXkZ7+NV0jnkSW2+cSG+qFmJp/7969xMTEYLPZ8PDw4Pvvv6dXr14EBARgsVh4+umnefnll1FVlZkzZ+J0OsnMzDzl+YYOHcpnn31Gjx49yMzMZM6cOVx22WUkJCTg6dn4cvGvvPJKg3EvwrlJKk7i9uW3AzB72OzarouWYFUlCoGrV05l110JZzzeXSKHPoJz8ydogAG7ltRu/wRI7BBFj9BR0EiwcjKrwcoTA5/giYFPnN/CnkZfTzPvpuRQ5HDio7twu1NljYbgyO71tjm72ckrTqA62fWhROtbf2ByS7SwdDDqMckSSZVnzrHSFJKkcX1a/ZOYqJcbbDvmnUNWeRGDQvvQtTiPRKurm3bN8Al8+Ww/FKeT1N9+5Zv/vseSuAT+tjyu9rnW/AIyP/4AS3R0k8pV5XCyb38uEjCio/dZ3VtzhfiEAIfIKS5qkes1pqBgM/G770av82PIkB/R65vfFd692/8Rt3Myf+y6jQH9FyHLZ5njpw1TVXur31ez35V69OhBfHw827ZtY/r06cTGxrJ//34CAgJYunQpy5Ytw8PDA6vVSlFREQMGDDjt+JXx48dz8803Ex0dzbhx41i+fDlFRUUsWbLklM955plnKC4urv1KTW18IJjQdCXVrjeE90a/R3ef7mc42r0+nfAlgQ4HwzTeLXpdqzWMjKnfkjN9A+VPHiJt+gYSbnif4qHT6JGRANs+bNHynK1AvStIyalpzm9PNBYdQQ/3p+MLw+n4ygi8RtdveW2plGi+Oi1lznNLYniChIxK085l6+xq3Rk2+Uo+vDSa+/V1gU7Orj94f/JEtn4+D7MqgSSRt801hkpVVTYs/Q6Ayia+SAsSMpBUCI30Oe2UZHfyNpvRSE5yy1ovsWNW1v9QVQfR0R+dVbACYLUOoF/fTyku/oO0tIVuLmHboCh2ZKl1W4+a/XFMr9cTGemaMz9w4EB27NjB3Llz+eijjxg7diyJiYnk5eWh1Wrx9vYmODiYiIimT6vz9vame/fuHD169JTHGAyGel1Pwrl7fYdrzMprO17DQ+fBgKABLXbtoOC+6CQtIXqvMx/sZqERdV2VFo9gOgVFQ9QU2PYxrHkRRvwNziEBXks48dbXHnKxnM6fu7skWi5gcU+o4iJJcoMuocYkpCRwcPdBJJ2Er4cvvh7QJziQ+KU/sMc7iIX//ieqRiajuhJFdr02qfdPw37zLaQPuZTIt/7DoWGXM3zqbWe81gd/pPCvJXsBeH50y31gkWUJL0MF+WXuab1qjpKSvWRl/0hm1ncEBV2Ll1efczqfn99IPD37UFracq3ELUlV7Uit3MJyzu3HiqLUG08C4O9f02y5Zg05OTlce+21TT5fWVkZiYmJ3HnnnedaNKEZevv3Jj43nuSSZGJXxLL4msX08ut13q+7bfcC5u35mHQNpNibORYpYzf88TmYfflPQRxGWYcqa7h95Ev4+ETgdDrYvPN9juXsZm/hQQYG9GfyVW8ga8/wR6eqoDWBT5c2H6xA3dgK3QU8fuV0gtfGAzQYqKo2su18qVIUyp1KTZK+c7vqqbqETkjOSWb+F58ilWox4cpR8vG/YunSfysarYPpXgYcGg3G66rpankQc1AMf3z2Kd0Hx6Akf0LRokVYFi0CoOOMhwgIOf04lL+u2MfP65IBuGF0BKM7t+zKW1ZjNfllLds6mJe/jt277wUgPPxRwrvMOMMzmq68IhG7vRCdzsdt52xtqupEVZ3I0gUUsDzzzDOMHz+esLAwSktL+fLLL1m3bh0rV64EYP78+VxyySUEBASwZcsWHn30UR5//HF69KgboT569GgmTZrEjBmuX5Ann3ySiRMn0rlzZzIyMpg1axYajYZbb73VjbcpnMnMITP5+6C/M/TLoVQ5qyiwFZz3a674fTZ/T/6WAEXlUp2Vf45tZhfMx3U5Jz4Lr+sqsK16lIcnzOPeJePYLTvQqipBCqzM/p0PP+9PJ0nH9D73M2LQXxs/r0YL/W6FhG+hLBc8zt9sJXewK66A5Vzz1bR1I31cY9pOBCpaCR7p3DJJvYrsrgDDHdPHJUlDaXUZP6YmUGS3sfz4GgoPb2bq8jQsTjPxg0Yh1fxr9qmsxF+rxWLIR6OteVNXdPT0fJCc3A9ItrwLZe9ivAlCuj2C+fY7Sf/bk5StcuUH0ladflHMSruTnze4BtouengYwzvWvcmmpJfi4aHD13p2CQ2bytvopKDyvF4CcHVplFckknL8E7Ky/wfAsJh1mEyhbrtGRPgj7N5zP+s3DKJTxzuJjHwKjabp08rbKkVx/e619oDiZgUsOTk5TJ06lczMTKxWK9HR0axcuZIxY1wzSg4dOsQzzzxDQUEBXbp04R//+AePP/54vXOc6DI6IS0tjVtvvZX8/HwCAgIYMWIEW7duJSCgbb9JtEeFVYVU1Sx7//qO1+kf2B+L7uzWt2mKX9PWAXCzTzTTr/+yeU/Orkss6Oh9Ax2Lt9KzuhqnJYAFlcksXTIKJzCnw9VMGv1vAOISFrEtaQWfF8Tzxb7PTx2wAFz+FMT9F46shP53NPPOWlZGlSu7qEnT9luDzkaQXotdUfm6X9NXZXY3BdC5KR40aMwUV27nH2tcH8pUSc8Nu7wYcKicxJEda4+7ZeBAetWkkji85BFSOUA364uEDXQ9r2t0LImrniPV/D8A9L4dkfV6Qt95m5TFSymeM4esTz9l3ZEjlNtsVKSmYT54gOouXVArK1ENRuJ9u2C2+dN9SHi9YAXgq1d24OGA+9++HL3+/A3m9jVDZsn5y++hKA7S0r8gKWkuDkcpAGZzJFFRc90arAD4+48i5tLfSEp+j7T0LyivSMRoCKnJ7CghnWgTrPmZmq11P9ccI/Gnn0/88jU8h1RzXN3P1B57+mPqzlu7/xQUxRX4ShdSwDJv3rzT7n/11Vd59dVXT3vMiSy5J3z9tXtX+hXOnr/Jny/Gf8Gdv9xJYnEiKSUpXOJ3yXm73nPjPmbDshv4sXAvDScRnsHBH13fJQ0rQiJJr4jjP92n0rl/LDHfjaNClnjKbzA3jKnLJzM4+k4GR99J3ILBOM40hsArBIKiIHljmw5YHIrKeyk5jPXzwvcCniF0OhLSWSeIc4eqmhw3wQb3NId/euUs1mfehFVvxKoz0M3akWNeW2HdU/R75h2u8rRQlpOD/yWuv72cxJ9I9f8ZAE9jXYuSxmDG7NEFFOgf/BF6S92CiGGTb+b7I8foufAz2LQegGqdjqQ+/fDduoVqiwVTcRG3Ff7AbUDJpPoDRQ8lFuBR06Dz0UfxPPzwILfce2P8LBoO5JxbwKKqKkVFO8gvWE9Ozi9UViaj0/litxcgSXpUtRpJ0tKl83Q6drwdgyHovE3RNZvD6d3rdUymzhQUbKCiMsm1JIOrpK4vVa35nT7xVbP55G31jnHdIyefA7Vm24njqXd8Y+do7Lp15zg9gz4Isyn8rF8Xd2if/+GEs+JUnLwWd/5zodjK81j02+PML/gDuyTxSFgzU3NXlcK2j1yPVSfmmvwI1vCReHh2YKpnTz4vPUiEX8O8Dlk5CezExu2ak1a7VVWoKACzb/0c9wE9IbdtLxGRbKviWGUVL3XreOaDL1Ct3dNVobiG3HYzu2egf7iHP+HdRtbblleTbDPuP29xzRv/wt+v7vezojIRAKlawtyx/rgyc3AvyICy4/H49qqf62rSs0+TGXsHVQYDSno6XSK60Ndqrd2v2u3sHH4ZlpJiip99Hscv36I1ubp/1v52HDsqts5mPPeV8N3PR7hhQje33P+f+XnoKbY1/a1IVRXs9kK0Wi+OJb3F8eONdyXb7a5ubS+vPoSG3k1gwNUtmqcoIvxhIsIfbrHrXQxEwCIArij7pmU3cbToKIOCBnF31N3nrXXlxfmD+cHTA2QZowqbMrewZ9FognwiiQkfT7ceE5HlU3zi2vAGrK6fg2dQdCzy4S94eM0MPpnwFX+/YSl35x7Az69hdk8/325EKjJHK9Phx4ddwc++7107fcJhwFToO8UVxCR8A4Hnf+DxuTDUDApuD6n5T6f12lfg0QOutAlW7fnrtoi+4jJ+uft+esz/hM0rxnD5xLq1abpEPUrJwk3kdtjJgRUP0W/Kd7X7fHqMQp9gIcX+OWE82eC8IaE1gWxgw+m6kk5H5M/LybxsOKFZx0hJOELE4D4oikJBUglYNMx8eiiv/n09RStT4TwFLP4eJiocWiqrKjAZXOM9FKWa4uI/kDUmdFoviop2oqjVFBfFkZX9w4k74MRvhkbjQUTEYwQFTqgZ7CohSXKrJzoT3EsELALgWszvaJFrKvl7o99DkiRif4nFpDMR5RfF5B6TCTC7YVyRqtKrqpofanIC6hWFHxyuMU367Gz+k7sZ363PMiVwKA9c/SHyyVlj9yyuC1aCoiDiCrhqDl4aLW93j+Xxwwv4+LfH+L8pv+Af0HiwpdMaCPLthi3nAMR/BQE9XONVio5D9j7X+U8OiMb/+9zv+Twy1kxnrXBTjpC26FSh2Kyj6azNL0WWXAmlZEmq+/6nbZIEGiQ0ksT0sACu8G36FPqjFTYAXu3e6Rzv5NRkWcYU7Oru0Xs1LFvU5C9Yu6EXWqn+kg6SJOFhC6XIerjZ18xJz2H3rbH4a/Tk3T2DqwZFUV3t4I3/7MBa5IRob2RZpmOULwXbcqlyODGch6AtwMsTqCSnOAd/i5Pk5PdOCkoa0ul86dhhCgZjCN7WQXh4tGzeKKH1iIBFAMDb4M2Q4CHE58Qz9Mu6lYMDTAHE58SzYN8CdBod0f7RzB42m2DLWa79JEnc9rd0bqgoQG/wQtZoURWFkrJ0TLKeuIQvWZP0C+/nbWfhwkFsit1d99zvptU+/K00kbmpBVQs+B/DDMG8cPtqjIcW8FVVGo9VFmBuZHG5E7qEDOIXWy72OzajM1nr7yzLgb3fQHEaDL4X/FpvoGdTGGtaWKrU9h2wNNbCsq6gFJ0MMd4eKKprYKxSM1ZAUdWan0FBdX1XVXaXVjLraAZrB3siN7FVKsVWTQ+LEWsTxwgpihOlZgq0U3HiVMCpOrEYjeg0pz6Hd1dXvirn00+hbN5QL+Gm4nANhjeZ64ImxekkadlzFAQfxJp56qVMGmOrtLH/xskEluQhvT+PMVcMAWDex7uxHK9AP9ife+925SWRVHBKYHeqGM7DO0aQ1QpU8vu2v9LRchCt1vVpxtt7CF27PomtMh0fn6FUVxdgNHZEp2v5fE1C2yACFgEAs87MvHHz2J+/n8k/TQbgqwlfEeUfxaGCQzy76VmqnFUcKTrCX1f/lflXz8frHBK9Gc11AYUky1i9XKP1h136OMMufZzFC/pQgsKDC0fwr2sXY/XqiGPCG1T9sYAVShGzzSrdVC05koP/OXIYvPFF/hF2DTPTfuaXjS9w45g3T3ntayKuYdGBRWzOi2dkaP2xBHgEQsxDZ31fLU1f08JSpbRmp0nrkIFLrR682K3pLR/fZRfy0P7jJFVW0dV85um6W4pKcahwqfXMs+Xs1VUsXLKEgmWLG91fEtaV+55+DtkpcTyzAnR6vL2M9Ap1Bc0DRsTw46BL8Uo93iA7eGW2qwUlL3c12h9fwaEtJ6PkO6oDqzDmWOl/87Izlu9kVaXlBBVlkTp0NGNrghW73UnxoWIUPx0P39u39tji8mpUCTyaGK0cOFpAXn4llw2tP66quMLOt3+k4eehp6LaydGcMgaE+bAywRWMFVRKdLTAiOFb0GhMdU+0DgTAYGiZKexC2yUCFqGeXn69WPSXRSTkJRDlHwVAD98eLJ24FIBDBYe485c7ufHHG3nzijdrj3GnFUkrah9vchYz5dvxhGhMHHGWU2R0vUH3r1Z4Yexcrlnnmpr8j8TFSKoKkkRXa+Rpz9/brzeX+F7CN4e/aRiwXGD0Na0E2VV20m3VNV0hEmVOJ5VOhSpFxaa4vlcpCtVqXWuDk5qWiBOxjlTX/VI7GVKidsKjhKsL4s8TIF3HSCcdc/JzpNpBs/XOLUmota0hrtkNSu3jupYSFcissqOXJRZm5Ne79yKHs9ljW1KKy/ArLSIxPYMMjQySVHNPUt1jqe7xv5Ky0DnsPN6EfC+L3nqNgp2u1PheU+5F1miQJFc31M7tW+l8KJ4lf70LAFnXFb2Ha/3muNvDuXNEF3b8uoZucVs5cO+DDc6tMfuiLdRS1iWPo3wKgCnfSi/L0wRPntrswaQVRcWucwwfVrvt2x8O42mHnuO61DtWlcCsSPyw/ChXX9UFg157yoU2FUVhzevxAFw6MASd1hV4rUjIZM6y/WQW2+odP4+k2sd2p45+/RbUD1YE4SQiYBEaiA6IJjqg8QXTevj2YPE1i3lkzSPcveJuPhn7Cf0C+7nlujuydjD9t+m1uWCev/R5ohQNC+LepEKxM8mzO129OtOj03B69pwEsoa9U/eQlbqZJ1bPYK/smoe5OX0j/Th1jhVJkqh0VJJaeuGvQSVJElathpeOZfLSsVMvMnqhsykqfz+U2iBA6WJq+lRjp9NJ7g9LuLm8lM1/NO05/YBeGi3FAyMJNp7+Wsao/rBzKwP+72Wu7Fv/76dXz55889k8TDkZeBbnozoLiLi/J0c/PUjpoiTWPP8iHTI3kxTZg8vvaZjl2xwYwcgbD6EodmwlqagV1VhG9WzinTe06b/f0FOS6Tb2itptKYcLUfQw+vL63UuXDu3IjoQS0n5M4bWfk7H7G/DMqSbg0kDuuKM3mpr8P0ePF/HtpwmcGGXzxIfbKTJJpBZUkJzvWivo4VGR3DY0jMSccrxMWr7ekcqVPQI5kpnCvZd/jUEnllwRTk1SVTetmd6KSkpKsFqtFBcX49XIgDXB/crt5Uz/bTp78/ay8C8L6d3IFOLmqLBX1I6dCTQH8smYT4jwbvoaVKqi8H9fX8VP9lwsisqWu/Y2+glwxuoZbM3cSpWzCpPWxPbbt59TuduCoxU2Mmx2nLXjNlQsGg1mjYxBljDKru8GWUYnS2hwDUbV1LTGnFiDqF4GB7U204Pru8pJ2RtO5Hw46UttkFWi3jlo5BwyrsGxJ1pnTh4oe6KlRq7ZZ5Clc56S6nQ6eeGFFwiO6EqvmGG4/vO5clmoak3OitrHrntKTksjbdtmTIMu5elrrj7t+ZesXk3qx28y+b0FdPL3a/SYrbtWsenVuWgtHXn0vx9RXF7NNw99j3fRUUxXejNm+v3o3JTv5VRW7jhGUOx1HAvqyg1r/1e7/aW/r0M2aHjmxcsaPKeo2MayNclk/5qOFqg0yXhVqBRqVbz7+RIdHcjviw/jW173djLf00aeRuWa6BC6BXoy/Yqu6LVi1o5QX3Pev0ULi3BWLDoL88bOY/LPk7lj+R2M7zKeW3veSmppKgOCBuBUnXgbvJuUKbfSUVkbrFze6XLeG/1es8sjyTKv3LaGJ3IPotUZTvnmti1zW20Lzn197mv2ddqiSLORyCaMxzgTqdEf2s906eJiVzfI5YMG0qvbmQdTq6rK5DVvUuGfTYm9kNXLf6sJzJTa75z0szG5hDHAf195EEmuGSqsqqgqSLKEJSgQdYcrDb7N4JpxZ7XosZkCyDIFcElO1nkPVpwlJfT87E0qFAfKpFtqt+dnleNdqkCpwvZlx/DwNRJ6iS+evq7fK2+rkTsn9cT2l0gURcVs0rF2Qwrbf0lGiitkb1whvoAUZSU03ErKshT6ywaefmIIkYGe5/WehIuHCFiEs6bT6Hh/9Pt8tu8zvjr4FcuO1R/452/y58lBT/KX8L/gVJ2oqsr6tPX8kPgD48PHMyp0FAatgRe2vABAsDm4ycHK+rT17M7dzUN9H0JzUs6WgIBTN5OvTF6JzWkjyi+Kp4c8TXcfMR3yYlFdXc3u3a4ZZycWZz2TRVsWccB7K6gSBrtMhV3Gld9DAuQG3/FRyQjVY5VAlVRX25AsISlgTqlATXEFK+Uefmh96vL7XD7YzuatDg4UBFM6YyET596KrHH/9OHKvQlkPvss9tRUNl46kR8dwdxUs2/rgRwAdBYtCRsysJVWo6oQHOHFdY/3R6tzlcd40sDbKy8L48rLwsjIKGXRVwdwpJRx8zVdSV6bgSRLzLl/AB1EsCK4kegSEtyi0FbIrpxddPXuyp7cPeRX5rM1ayub0jfhofOgzF7WaIr1E9s0koZdd+5qcrN/nwV1S8H/cecf6M6wxsUf2X8QuyIWb4M3q25ahVF7fhd0E9qWn376ibi4OPz8/PjrX//aYBZOY95Z8w4fp37MzxN+Jsy/edOG/2z+wpcozc3hmknT+N83e6nKVXnmX3ULvDoqbXz72PfkSUHE/j0Sj67ndr0/K9uwkdT770fftSsh/5zDwnJv3llzlL2zx2GzO7n5wy2oqCybMQJJkrCV20n8I4f1Xx2m39gwYq5v2vT+9YsPs3dtGsERVm58aqBb70Fon0SXkNDifIw+jAobBUBnr84AxPaOZWP6RjZlbKK7T3dmbZ4FwJ2X3ImvyZe47DjKqsvw0ntxW8/bmjVGoaNHR9LL0gF4aetLzB42u97+vMo8Zm2eRWyvWIaEDOG5Tc8B8M9h/xTBykWmrKyMuLg4QkNDiY2NbVKwAhDuFw6p8OOeH5kxasY5leHuO/5R+9gQvBttoj8V5TbMFtfvotZkxMtUTUFF9VkHK6qiYE9Lw5aQgMbfH2deHmUbN1GVeBTb7j0ARHz/HZJej25TEg6n68PDJ+uPcSirlG+mx9T+DRotOnpf1pHK0mq2L0uiSx9/QrpaG71u1rFidvycRGWpndyUUsJ6+3HlHQ2zTAvCuRIBi3DeSJLEZZ0u47JOrkF8g4IGMeH7CXxz5Bu23bbtrMeQxGXFkV6WztReUzFoDHyy9xNMWhNPD3m69pjDhYdZn7ae9WnrGddlHCmlKQwKGsSVYVe65d6EC4dSsxZQampq7eOmWLR3ESaHiRv63ODW8gwdGsW2jXn8vnUn40cPr92eWuaLHhuK3Y6sa96quI7CQtIe+iuVu3Y12GcaMICgZ2ZijolB0rvGyFgMWirtToa/uob0okom9e9IdCfvBs/tP64zR+Jy+Ond3Yy5pxdd+tR1p6mqyubvEolflYLBosXk4Tp3lz5+ePiIDwWC+4mARWgxYV5h3NfnPj7d+ymJRYlE+pw+X0pjcityuXvl3QBcH3k9kd6uc3yy9xNWJK/gnqh7GN5hOAMCB9Q+Z2XySgD3LC0gXHC8vLzw9fWloKCA/Px8QkJCzvgcp9PJQftBRvmMokNAB7eWZ2BkH1ZZlnDgD5Xxo+u29++rYfs+C1seeYtLLuuI96RJyKaGOUkcBQWU/roKpbwMXYcOKBWVZD7/PDidBP797xi6d8eRm4vs6YHnVVc12nJ5bd8OVFQ5yC6tIjGnjNhhXRotq0YjM+lvA/ht/n6Wf7CXm54eiH8nD2SNzOFtWcSvSqFzlB8xk7riFWAi/VAhnXr4uOulEoR6RMAitKjk4mQAKhwV9bbH58Tzxs43iLBGMDRkKIqqYNFZKLeXYzVYuTTkUrSyln35+2qfI0uuAY8P93+YUM9Q1qWu4987XGv/+Bhc/zSHhgxl5uCZ2BU7PXxFM/XFqnPnzhQUFLBp0yZuuummMx6/+sBqHLKDQR0Hub0skiRh7OpAPeCFw+lEWzPANnRwZ7bvy6bgeD7Za/+Lbfce9OHhSDotnmPGIBkM5L7zDqUrf0UpLQWtFhyO2vN6jr8av3vvaVIZjDoNdw0Pb9qxFh3jpkXx/et/sPSVOJDAaNZhK7cT0tXKhIeia2ZFUa8FRhDcTQQsQos6VHgIgEfXPspzlz7H8qTltS0gAGmlaXx75NsGz+sX0I8v/vIFV4RewfCOw9mUvgmH4vpnLUkSk7pN4vrI60nIS6CkuoTXdrxGYVUhO7J24Kn3JMgi0npfzJKSXBlVY2JimnT8WwlvAbAifwXb1m5zBcd/muKt1uRwkZzwxG83UKGrItWag6xKSCe+FKnmZ5BVGUmVyFSKKFNTwWrklX/uq1krScUpVeFhCiY8eTnet06h6KuvAZD0enJee931WKfDN3YqPnfcgdbPD2dJCaWrVuF51VVo/BrP/eIOOr2Gax/px/afkkCC7KQSHHYngyZ0qQ1WBOF8EwGL0KLeGfUON/x4A3mVeTy69tHa7aNCR/HKZa9g0prIKM/AQ+eBzWHDU+/JGzvfYPGhxaxIWoGn3pMdmTu4MvTKBi0mkiTRJ8A1e6ird1fGfDMGRVWYtmoaP1x/6tVfhfbPaHSNqfjxxx+ZPn36aY8tKiqiZ2JPCjoUYNFbsDltKIrSaNeKJEnoFdd4E7PdgE7SocgqSCpOGZAUVBmQQZGcIEFxtg0tWiQfAwH6YHRaLbIkczRrD2XWLOwRnQmZNQu/e+9FtliQjUaKvv0O2WLBMiwGXVBd8K3188NnyhS3vU6nY/TQcfkUkQpAaD1iWrPQKuxOO/P3zSfEEsKYzmNOO3OnuKqYm5fdTGa5K/V8TEgMc0fNxaQ9/ZojhbZCFh5YyMhOI0+51IBwcSguLmbFihUcOHCA559//rQzhRYuXMjRo0d57LHH8Pb2PuO5VVUlY84WVJsTUx9/PEeFoQ85dcLE11/6N2X2CmbPnl1v+ztvvUV+URF3TZxIl4FiSrBwcRDTmoU2T6fRMS16WpOOtRqsLJ24lJXJKzFpTYwPH49WPvOvro/Rh4f7P3yuRRXaAavViizLBAQEnDZYcTgcFBYWEhwc3KRgBVytLB3+MZSKXbkU/ZJE5d48fG7shj7UE11ww8ClzO4av7Xl27XIsky/KwZj8DHTOzqa9evXE9Dz7NcIEoT2TCzsIFwQrAYrt/S4hYldJzYpWBGEkzkcDg4fPkxlZSWff/45hYWFDY6x2+18//335OfnM2bMmGadX9JpsAwJJuB+V5dk4bdHyJ77B+VxWQ2OHRHhGsi7cu/v/LJ7LVu/WwfAoUOu8V179uxp1rUF4WIh/vMLgnBRkGWZsrIyysrK2L17N1dccUW9/evXr+fgwYOMGTOGrl2bltn1z/QdPOgwKwbVoVD00zGKfkjE1MsP2VyXV+WqqdcwyvkXkODzt+axL+0wg8pHkp2dDdCsXDGCcDERLSyCILR7Wq2W6dOn06GDK6dKdHT9MU1Op5P4+Hj69evH8OHDGztFk8kmLRpPPdaruyDpZPI+24ezrLr+MRrZ1R3Ury85ahE/f/597b7Q0NBzur4gtFciYBEE4aLg7e3N4MGDATh69Gi9fYmJiZSWltK/f3+3XU/rY8Tvrt448m3kf74f1dGw5aT3Zf0xSwb2Zx9Fp2p4bPojhIW5dx0hQWgvRMAiCMJFo7S0FIADBw40ut1iOfXsnrNhCPPCL7YX1elllKxNbbBfp9Px+My/8cTUGdxedRnEFbn1+oLQnoiARRCEi4bZbAbA709J1nr37g3Apk2b3H5NQ5gXxh6+VKeWNrpfZ9DjFeGPZ3QQZRvSyX5nF/a8SreXQxAudCJgEQThonDo0CF++uknJElqMAvIaDTi7e1NXFwcVVVVbr+2pJVAOX3KK68xnUEjYU8vg0a6jwThYicCFkEQLgrLli0DXIne3n77bXbv3l1v/9ChQwE4ePDgebm+s/j0gZAuwIwx0hsAxxmOFYSLkQhYBEG4KHTq1Kn28YmcK3l5ebXbYmJi8Pf358iRI26/ti7QjFLpOO0xlYcKsB1y5YcxhHq6vQyCcKETeVgEQbgoTJkyhcLCQjIyMkhLS2P79u1YrVZsNhvr1q1j3759lJaWYrPZ3H5tSadBtTfs5lHtCorNgepUUGsCGn24FTRiQUFB+DMRsAiCcNHw8fHBx8cHVVVRFIV33nkHRVGorKwkKioKVVVru4bcyVFoQ61yUrD0MCcWfZYkifIdDTPhVicVU3W0CFNvf7eXQxAuZCJgEQThohMVFUVgYCDbt29HlmWGDh3aYOaQOxl7+FB1tAhHrmsdIVRQAV0HC6Yof/SdPLEdLqRsYzqSXkbfWSziKgh/JlZrFgRBaANURcWRV4nGU49sEp8lhYuDWK1ZEAThAiPJErpAc2sXQxDaLDFLSBAEQRCENk8ELIIgCIIgtHkiYBEEQRAEoc0TAYsgCIIgCG2eCFgEQRAEQWjzRMAiCIIgCEKbJwIWQRAEQRDaPBGwCIIgCILQ5omARRAEQRCENk8ELIIgCIIgtHkiYBEEQRAEoc0TAYsgCIIgCG2eCFgEQRAEQWjz2sVqzaqqAq5lqgVBEARBuDCceN8+8T5+Ou0iYCktLQUgNDS0lUsiCIIgCEJzlZaWYrVaT3uMpDYlrGnjFEUhIyMDT09PJElq7eK0mJKSEkJDQ0lNTcXLy6u1iyOcRNRN2yXqpu0SddN2na+6UVWV0tJSOnTogCyffpRKu2hhkWWZTp06tXYxWo2Xl5f4426jRN20XaJu2i5RN23X+aibM7WsnCAG3QqCIAiC0OaJgEUQBEEQhDZPBCwXMIPBwKxZszAYDK1dFOFPRN20XaJu2i5RN21XW6ibdjHoVhAEQRCE9k20sAiCIAiC0OaJgEUQBEEQhDZPBCyCIAiCILR5ImARBEEQBKHNEwHLBeCll15i2LBhmM1mvL29G+zfvXs3t956K6GhoZhMJi655BLmzp17yvNt2rQJrVZLv379zl+hLxLuqJuNGzcyfPhw/Pz8MJlM9OzZkzfffLOF7qD9ckfdfPfdd4wZM4aAgAC8vLyIiYlh5cqVLXQH7Zc76iYzM5PbbruN7t27I8syjz32WMsUvp1z1/vNunXrGDBgAAaDgcjISD777LNzLlu7yHTb3lVXV3PzzTcTExPDvHnzGuzfuXMngYGBLFy4kNDQUDZv3sy0adPQaDTMmDGj3rFFRUVMnTqV0aNHk52d3VK30G65o24sFgszZswgOjoai8XCxo0beeCBB7BYLEybNq2lb6ndcEfdrF+/njFjxvDyyy/j7e3N/PnzmThxItu2baN///4tfUvthjvqpqqqioCAAJ599lkR4LuRO+omKSmJCRMm8OCDD7Jo0SJWr17NfffdR0hICOPGjTv7wqnCBWP+/Pmq1Wpt0rEPPfSQeuWVVzbYPnnyZPXZZ59VZ82apfbt29e9BbyIuaNuTjZp0iT1jjvucEPJBHfXTa9evdQ5c+a4oWSCu+pm5MiR6qOPPuq+ggnnVDdPPfWU2rt373rHTJ48WR03btw5lUl0CbVTxcXF+Pr61ts2f/58jh07xqxZs1qpVAI0Xjcn27VrF5s3b2bkyJEtWCoBzlw3iqJQWlp62mOE8+NMdSO0nj/XzZYtW7jqqqvqHTNu3Di2bNlyTtcRXULt0ObNm1m8eDE///xz7bYjR44wc+ZMNmzYgFYrqr21NFY3J3Tq1Inc3FwcDgezZ8/mvvvua4USXrxOVzcnvP7665SVlXHLLbe0YMmEptSN0Doaq5usrCyCgoLqHRcUFERJSQmVlZWYTKazupZoYWklM2fORJKk034dPHiw2edNSEjguuuuY9asWYwdOxYAp9PJbbfdxpw5c+jevbu7b6Xdacm6OdmGDRuIi4vjww8/5K233uKrr75yx+20K61VNwBffvklc+bMYcmSJQQGBp7rrbQ7rVk3wum1l7oRH7Vbyd/+9jfuuuuu0x4TERHRrHPu37+f0aNHM23aNJ599tna7aWlpcTFxbFr167aQVGKoqCqKlqtll9//ZVRo0Y1+x7aq5asm5OFh4cD0KdPH7Kzs5k9eza33nprs67T3rVW3Xz99dfcd999LF26tEFTt+DSWnUjnFlL101wcHCDSR3Z2dl4eXmddesKiICl1QQEBBAQEOC28+3bt49Ro0YRGxvLSy+9VG+fl5cXe/furbft/fffZ82aNXzzzTe1b5SCS0vWzakoikJVVZXbytBetEbdfPXVV9xzzz18/fXXTJgwwW3Xbm/awt+N0LiWrpuYmBiWL19eb9uqVauIiYk5p+uKgOUCkJKSQkFBASkpKTidTuLj4wGIjIzEw8ODhIQERo0axbhx43jiiSfIysoCQKPREBAQgCzLREVF1TtnYGAgRqOxwXahec61bgDee+89wsLC6NmzJ+CaSvv666/zyCOPtMo9tRfuqJsvv/yS2NhY5s6dy9ChQ2uPMZlMWK3WVrmv9sAddQPUPq+srIzc3Fzi4+PR6/X06tWrpW+p3XBH3Tz44IO8++67PPXUU9xzzz2sWbOGJUuWnPsYpHOaYyS0iNjYWBVo8LV27VpVVVV11qxZje7v3LnzKc8ppjW7hzvq5u2331Z79+6tms1m1cvLS+3fv7/6/vvvq06ns3Vuqp1wR92MHDmy0WNiY2Nb5Z7aC3f9T2vu/z3hzNxVN2vXrlX79eun6vV6NSIiQp0/f/45l01SVVU9t5BHEARBEATh/BKzhARBEARBaPNEwCIIgiAIQpsnAhZBEARBENo8EbAIgiAIgtDmiYBFEARBEIQ2TwQsgiAIgiC0eSJgEQRBEAShzRMBiyAIgiAIbZ4IWARBEARBaPNEwCIIgiAIQpsnAhZBEARBENo8EbAIgiAIgtDm/T92cF2V/RHKhgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filling enclaves for HD 5225 ; pop/aDP will now be 696699.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filling enclaves for HD 7756 ; pop/aDP will now be 887932.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filling enclaves for HD 7967 ; pop/aDP will now be 851150.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filling enclaves for HD 8999 ; pop/aDP will now be 699604.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filling enclaves for HD 9013 ; pop/aDP will now be 712684.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sHE = stillHasEnclave.copy()  #note - this block modified slightly since running; ignore the plots\n",
    "for t in sHE :\n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    HDvPop[t] = np.sum([unitPop[u] for u in HDunitList[t] ])\n",
    "    ePop = 0.\n",
    "    \n",
    "    for i, eL in enumerate(enclaveLists):\n",
    "        for uu in eL:\n",
    "            ePop += unitPop[uu]\n",
    "    if ePop + HDvPop[t] < 1.2 * aDP:\n",
    "        for eL in enclaveLists:\n",
    "            HDunitList[t] += eL\n",
    "            for uu in eL:\n",
    "                unitUse[uu] += nDistricts * HDweight[t]\n",
    "        HDvPop[t] += ePop\n",
    "        print(\"filling enclaves for HD\",t,\"; pop/aDP will now be\",r5(HDvPop[t]))\n",
    "        stillHasEnclave.remove(t)\n",
    "    else:\n",
    "        \n",
    "        for u in HDunitList[t]:\n",
    "            plotPoly(unitGeom[u])\n",
    "        for i, eL in enumerate(enclaveLists):\n",
    "            for uu in eL:\n",
    "                ePop += unitPop[uu]\n",
    "                plotPoly(unitGeom[uu],0.2)\n",
    "                plotCenter(i,unitGeom[uu])\n",
    "        print(\"here are the enclaves for HD\",t,\"w pop/aDP, ePop/aDP=\",r3(HDvPop[t]/aDP), r3(ePop/aDP))\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba2a282d-24d0-4e9c-9c94-be413ad93369",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "42ddb4c6-a84a-45ec-be3e-bb9d1af51e20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final (?) check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 801 time is now 9 sec\n",
      "working on HD 1603 time is now 62 sec\n",
      "working on HD 2405 time is now 123 sec\n",
      "working on HD 3208 time is now 130 sec\n",
      "working on HD 4008 time is now 150 sec\n",
      "working on HD 4808 time is now 158 sec\n",
      "working on HD 5609 time is now 186 sec\n",
      "working on HD 6409 time is now 207 sec\n",
      "working on HD 7212 time is now 211 sec\n",
      "working on HD 8016 time is now 215 sec\n",
      "working on HD 8816 time is now 241 sec\n",
      "out of 9111 total HDs, there were 9111 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 392 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final (?) check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "7e8a7e3c-3e65-4e58-a781-b051edff3dde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00199 0.1255\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "f4bb76c7-0ae0-40cb-9c6a-ee436dfc30c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDunitList = [savedAgainUnitList[t].copy() for t in range(nHDs)]  #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "1.46924     0     1204 0.5\n",
      "1.92639     1     13195 0.5\n",
      "1.20795     2     3236 0.5\n",
      "1.00829     9020     179702 0.25\n",
      "0.85227     9021     27743 0.25\n",
      "0.95469     9022     44076 0.25\n",
      "1.06663     9023     41430 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )\n",
    "# note for MN, option 3 here led to tight CCB unit usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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jY8jfvrW9myIIgiAIZ7wzKggBiEhKQaPXUVdZ3t5NEQRBEIQz2hkXhACExydRnpPd3s0QhHYhBqYLgtBRnJFBiM5oxO10tHczBEEQBOGMdkYGIYriRWcwtnczBEEQBOGMdmYGIR4vLocDVRXjBARBEAShvZyRQYhWrycuvTu7ViyhrkIkqAqCIAhCezgjgxCAgJBQ0oePpq6inD1rVuISM6gKgiAIwkl1xs0dc7DYLumoikLuts24nU4ik1Owhke2d7ME4YQRDyEFQegozvggBECSZZJ69QWgYNcO6isrie2S3r6NEoQTRAzRFQShoxBByEHiunajqriQzA1rALCGRxIWn4gkiT/dgiAIgnA8HVNOyLPPPoskSUyfPr1xncPh4M477yQsLIyAgAAuvvhiSkpKjrWdJ1VIdCyd+g2iU79B6AxGdi5bjNMmckYEQRAE4Xg66iBkzZo1vPPOO/Tu3dtv/d13381PP/3EV199xeLFiyksLOSiiy465oa2l6DIKNJHjCFv+5b2boogCIIgnFaOKgipr6/n6quv5r333iMkJKRxfU1NDbNmzeLf//4348ePZ8CAAbz//vssX76clStXHrdGn2ySJOF1u3E01Ld3UwRBEAThtHFUQcidd97J1KlTmThxot/6devW4Xa7/danp6eTmJjIihUrWjyX0+mktrbW76sjSu7Tn/KcbHavWkZDdVV7N0cQBEEQTnltTkz9/PPPWb9+PWvWrGm2rbi4GL1eT3BwsN/6qKgoiouLWzzfM888w2OPPdbWZpx0BrOZ+O49AcjZspHaslJiOndt51YJgiAIwqmrTT0heXl53HXXXXzyyScYjcdn7pUZM2ZQU1PT+JWXl3dcznsiJfXqiynQSu7WTe3dFEFoM1EnRBCEjqJNQci6desoLS2lf//+aLVatFotixcv5tVXX0Wr1RIVFYXL5aK6utrvuJKSEqKjo1s8p8FgwGq1+n2dCoKjYzBbg8hcvwZF8bZ3cwThiInB5oIgdBRtCkImTJjAli1b2LhxY+PXwIEDufrqqxu/1+l0zJ8/v/GYXbt2kZuby7Bhw45749tbeGIyCd17kbl+bXs3RRAEQRBOOW3KCQkMDKRnz55+6ywWC2FhYY3rb775Zu655x5CQ0OxWq383//9H8OGDWPo0KHHr9UdiM5oJDAsnIKd24lL797ezREEQRCEU8Zxr5j68ssvI8syF198MU6nk7POOos333zzeF+mQ4lKSaWuspy961YREBpOVEpqezdJEARBEDo8SVXVDpWnVltbS1BQEDU1NadMfsiB9qxdReqAwaLMu9BhZW9cR3LfAe3dDEEQTjNHc/8+prLtQnORyZ0oy8lq72YIgiAIQocngpDjzBIcjK22pr2bIQiCIAgdnghCjjONVoejrlYEIoIgCIJwGCIIOQG6Dh9N/o6tuBz29m6KIAiCIHRYIgg5ASRJIrlPf4r3ZLR3UwRBEAShwxJByAmiN5rwul3t3QxBEARB6LBEEHICiXLugiAIgtA6EYScQF63G1VR2rsZgiAIgtAhiSDkBIrv3ouCndvbuxmCIAiC0CGJIOQEKty9k5gu6e3dDEEQBEHokEQQcoIU780gIDgEjfa4T88jCIIgCKcFEYScILaaaqLTurR3MwRBEAShwxJByAlQWZiPNTyivZshCIIgCB2aeFZwArhsNszBIe3dDOEMoaoqe7esw2g0E9+lOzjrcGbuwZlZgD6tM7aVKzH26IF54MD2bqogCIIfEYScALbaGqJSO7d3M4TTnKIqlNvLeeuJvxGQaQNAkgMIDxrFsKBfiCxZT9bKpmA4asb9hF5/PWp7NVgQBOEgIgg5AVRVQZKk9m6GcIpyuP2L3O3/VZKQGpdzKzP57c4L6FSoYI5LbQwsJKWWuvqVrJSv5qrYZci6oMbzlL34NPqESKTg0JPxMgRBEA5LUlW1Q30wqq2tJSgoiJqaGqxWa3s356hUFRfiqKsjpnPX9m7KYW1bUsDGP/KoKbWhqpA2IJIJ13dDq9cc9TlVVWXBggXk5eXRo0cPBgwYgCyL9KMj8dQv23lvSdZRHdtDyuZz/RMESoeeODHLEU3KjAUQFHdU1xEEQWjJ0dy/RU/ICRASHUtG3or2bsYR2b60kOoSW+PynnWl1FU6GHlpZ6I7BR3iyNZlZGSwZMkSALKzs9mwYQPx8fF06tSJ9HRRN+VQyuuPfr6haZoVhw1AAPA4qcndQFAvEYQIgtC+RBByghhMFmrLyzr8KJlJN/Xgk0dX+q0ryarlm+fXIUlwxcNDCI21tOmcq1evbvxep9NRWFhIYWEha9eu5f7770ev1x+Xtp+OdhTVNn6/csYELAYNbq/Kt+vzWZlZSUZpHR6vSo9IE7FWA2f1iadrpAVVUQn7z1UAFIQMJub6j7C/fh0Wz6oWr7PAEcKFJ+UVCYIgtE4EISdIYs/eZKxa3uGDkMBwo9/yuX/vw/zZO7DXu1EVlc8eX0Vcl2BMgXrCE8wERFQQ2yUQp7MIkykJi6VT47FlZWXMmTOHvXv3AnDnnXdiNBrZvn07v/32G4qi8M033xAYGAiAJEnIsky/fv2Ijo4+eS+6g/poRTY7i+sal99ctAdFVfltSzEVDf49JAXVvh6P2WuLOLdPLK9d1rNxW0T1RjxPdMcS2JRb0qAaWKt05S73nXRx7+VvST0RBEFobyIIOYG0ej2qoiB14HyIrI3lfsuJ3cO48fmR1JTZ+PhhXw9Jwe5qAPas8+0T1f9tQtIWAZCWeh8JCX9h9erVzJkzp/E80dHRRET4ArAhQ4awZMkS6uvr2bVrV7M2lJeXc+211zZbn7l+Dd899xgBYeGcO/0+Yrt0a/E1OOrdZG4sIzI5kPD4wDa9/o7Eq/inZ324IsdvOSXcwv1T0nG4vewqruPNRb5g76dNhdw+phMNnf6GI2Mxa5R06o1GOnmKmLxrFYucfXkr/nwyYxKRJIXctDQKShog8ugetwmCIBwvIgg5gaJSO7NrxRK6Dh/dYUfLRHfyTx7yuhU0OpmgCDPXPzOCH19bSlWhf9uNoU2Jk3v2Psf69Z+wadO4xnXTpk2jV69efsdcccUVZGZmoqoqqqqyYcMGampqALDbm+cx1JaX8t1zjwFQX1HOZw//k24jxxLfvSfJfQY09jA5bW5m3buk8bi/vjkOWe6Y7/Xh3DAiheRwC1sLagi1GCistqOoKomhZgYmh5AW6R9gpYRb+OfXmwGY+upSYPi+rybL5O7ctftrrvIu5S+v/Y9hbyxBm1mAeWCHykcXBOEMJYKQE8hsDSK57wCyN60npe+A9m5OiwJC/B/H2OpcBIYa920zoEjFQEzj9ssfTSM8ZgFudy1/LukHgDUoH1ABiZtvvpmEhIRm14mPjyc+Ph6A7du3NwYgAMOGDWu2vzU8stm6HUsXsWPpIrQ6Pekjx9Jj7Lls+N13Hi8q2VqFL5ZmI+tV6nI2kWeTGNx/IOf0juuwQeDBxnaNZGzX5q+9JZcOTCA+xMyV761sdZ85yUNwpyZy183nIGs0dAs0k7nHjdsrghBBENqfCEJOMKMlAKMlgNytm0js2ae9m9NM4Z5qv+X9Ach+oy/vz0//LmpcXvzlYqbddgkZezL89ps69Rx69+6LwWA47DWNxqZrTJo0iV7yHnjhQlC8YK+EG36B0E6MiitjSUFTTk1oXAKVBXl43C62LvydrQvnodH3RGsey9KQXczQf4DxDxcutPzqHYqki+KN3N28934E7/11LBFdU9ry1pwShqWG8XVaLX/d7KVOb8Yr+R79uTU6ZMXLR7cOY0Ra03uo0/i2uzxKu7RXEAThQCIIOQliOndlz9qWRym0J8Wr8N2L6xuXh5yXQl11MVUlVZitZoyBOmJSErji0XC+eWE5blsgxTvi2LF6JZqQpnwCWZrCoEFDjvi6nTp1wmg04nA4yM3NZUTRPGgoa9ph9lQAels0bNNbqHSZAVBrC7nw+ovxBqew/Ps5lOdsxevaQkCYhQf139BHzmw8RbxUzmvcTJqjjHpvH5a8mU3vC7SkjWveS3OqC/nqAz7M8c8fybeEE+RqQIq7n42DhzcWM7Nl1xCBhMvjbX4iQRCEk0wEISdJcFQ0VUUFhMR0nNoMv7yxufH74CgztbbFfHh/4kF7KchaF4qnKR8hsWtXQqNimb9g3x7qb1RV5RMSEn/E146PCmVPTiHGrHmg7ktotUT4BSNGjZcbh9Tz0hJfEFJV5+W7D74hNsjD9e/O4dvnPydr3cfUFK2hT3Cm3/ktqofg4i4EVn+KPrA3Rdo4ir7IILx7GMFR5iNu56nAdVAAAhDfsC/h+K1HsWS/iGzw5f48h54iTBQV2podIwiCcLJ13GEbp5nwhCSqi4sOv+NJZAlpenRSXWJjxx8HByAAMoqn6fFJ0vBfsdnyAHC5AhrXr98whrlzJ5CTvaTZGQ5WX5yFs3CH7+yuGnA3+DZc8BbcmwE3/AoPlcHMGvjbau544116pjYFQYU1WpY8cysX3HsZBksc4GWXs+lRV4k7jXX1l6KTopE0sbjqvsBZ9yXuht+x11UeyVtzSum6cQPmwYOpDglotk0OTkbSmSmRVUpkFZvk6xPpLWq1CILQAYiy7SfR3nWrSB1w5I8tTjSvR2HXymJKcmopz62jNKepRkXpgC8oqU5iXVVnBkdu4ZyEJWj09egsvpu4uzYdnXVns3Pa7YFMm7qx2XqPy8Hqr15hc1YpxZ6mn+tQSy5nX3E7mEIhPO3QDVZVXrri3MbFKeePplrfmxVfvQ6SgbPueJSeo3sD8MbtCxr3c9Z8gKpUNC7f9fF3aHW6Q1/rFDTxxw956Jl3iaryvdbK4TeQFOkbLWO9LQJrSjo1v2Wx+7c/6TRkMKFXdEXSiM8hgiAcH6JsewdWsGsHUSmHucmeZBqtTPeRsXQfGQuA01HJpnVP8+JqK6v3nte43y/FQyhRddyYuABZZ0ejt/sFIOttGvqbfTkGZaXJuF0edHr/X62f33qEjVVmwPeLGSzbSA7RMvS8eyDhCAtnSRIRASpl9b6RLtuWLOKi1+5hzY+f43GWM/eNB1n9XX8ufuBuOg/0krF2//w3/nF2adaeVmuOnMru7m7liqdeI829m9t0b9DFtRIW+YKQio+2E/BgZzShRnSynsxVq6lbtwt1qInkSyefMqOHBEE4vYiekJNg77rVmK1Bp8SEdori5PXfPuLfS2L81kdatjClOopVPb4iOSiTUI3KToeGPJeMQ/XdwGRUennC6Fc+kLiwFLoNSMJkMaJWZfPTkk0A9LdW0mPgSIyBIYDE/qG9Xo+X+nIw6KxIsowsy77/azTIGg2SRou9NJeP3/yssU03PHQfYb1GUZZbyI8v/Yfq4m2+dmiDmXzrP1n4aTGSpMNV9xWKJw+t3kCPMeMZf9PtyPLRT9DXkZWWzWXHjvvweHy9WlHlVxG8fjIAHsmG3e0iUBuMW3FRYs+mRG9jpdXJsHMv5bz+Se3ZdEEQTnFHc/8WQchJkLFmBcm9+6EzGA+/cwfx1aY1vLFwA9nFcUi6cgLSXvTbPswzkZsHX4+sh/vnPYQmNIv7oh1Hfb2N77S9Z6L76PFMufOexuWti9Yy772XUTw1IOnRB16LrAnC3TCXcdeNoceYiWjPgFyIzMz/kJX9KgDDhi6g+P1l6PMSyJbL+EPflIx8pWMEc6RV1DnLWBK9l1r9NL6/6QairKfO76kgCB2HCEI6KLfDQdbGtYTGJRCe0PE/bW4q28Q1v17TbL1G0eKVPZiVAJZfvxTNvt4El9vOkiVNj1TcThOKqkGS2NfZoSCpCkac6FpKQVBh5Wz/2XVNWi8qoKigqhKKCooqoeL/2GDa9PuJ6dIVa5ivFkZ1aSUf/us+3PYiNMah6EzD6TFSYuw14zhTZGQ8TW7eLADGjN6ELJtZ/fH1zMlM9d/RK6H1BlBiLmZj0HyCGnQU1NzPDRN6cuvoTpj0p2dvkSAIJ4bICemgdEYjXYaOpCRrL3vWriKpZx90xo77aTPZmowiW7EHjAFkDLZVrL/8RwyyAbmFeXD0OhO6oDG4axYDoNXbSQh9FI/bjjEglIiogVgDwsHQ+rwuZs1XLJj1AQDWWA23vPxzi/uVZO7h4xnTG5d/fuVZADr1H8yF9z1CcGQoqf37sHNZEShl9JkQy/CLO/5jsOMpLu7KxiBk2fIx9Os7G2tkF8j0rw0yL34hLhUcJl8ia3Wgm8Cicr7/YS5frurMvef04Pw+cadsGXxBEDo+kRp/EkWlpJLafxAZq5fj9XjauzmtsuqtSMn/wRZ8GbbgS6iKfYGpy+dRsOUrmBnE7+9O5W+fP8fFS3/gmxxfsbPRA/5H9i5f5CtJkF/1GMX1z5NdfD9rNk0kO3PZIa8Z2ampxkhSv9aDhqhOaST16tdsfeb61aiqiq22ht0r5wEw7rppjLw0/Yy7iZrNKXTv9jxuVUu9q5pvll/Es5W/8mPij+SZ89hj3cMvsQu4b8wzOOWmwDBEGsADN0/FHdsVXcleXpr9Cxe+uYx1OaffsGZBEDoG8TimHbhdTvK2bcZoCSS2S/rhDzjJ/pdfxgMZBc3WFy8e47d8Wa8X+TN0EJ3lXJ5K787y/z1DUEE20YPKsETaQZLR6H3BlqvOwvizlmIwtvwzLcpcx6czHgXg9nffR6M1YzCbWx21UVNWQua61VQVF7Hhtx8B33wzQy6+nHnvvAbAPZ//dMaM+qhx1lBiK6G4oZgKewV5dXm8t+W9VvePlodRrKxoXB4WcgNvT7sbWZZxuL38b1kW783bRkhtLnXaQIYM6s39U9KJDzm9Cr0JgnD8iJyQU0xJ5h4aaqoICAkjMrlTezcHgAUVtVy12b/66K1hxfyvVM+sHS9wVsXyxvUT+7/H1sAujcvJeRmMW/4r4VVljLjv7wztP5maiixWrjobrdGDp3QoZ13xSYvXLc3bzEf3PtBsvTlM5aon3iQorOVcGpfTwevXX0pLv8aTbv0/ek8464hed0dT56rjX3/+i92Vu4k0R2LSmSi1lRJmDCMlKAWrwUpxfTE5dTkU1hdS7aw+6mv1CJjC5xc/32x9aZ2Df/++m19XbCPCWUa1OYrLx/XhjnFpBBjEk1xBEPyJnJBTTFQnX92Qivxcdi5bTNfho9v9k/tXxf5d73clRTGjU18e8ips6NuVOavfJ7QyG32vS3gzpR93bVnHBpevzkh2Qmc2dxvEfcMTGdrfNyw0KCyFUNM11KqzkUNXU5S9nJjk4c2uGxrdlYAoqC/xX2+rkKirzPcLQlx2G3vWrMRgsRAal4DebMHZUN/snMFR0cf6drSLSkclY75o6nUqtZc2fp9Tm8P60vUtHYZVbyXMFEZcQBxGjZH+Uf3pFtqN9NB0Br59O7qgTXgd0aieQLz2RGRDMZG67nx23cMtni8y0MizF/fmumHJPPHzdmp37GTuL/P4btUe7prSm0sHJqA5wx51CYJwfImekA7CVltDwY5tBIZHEJ3aud3a4VVV4hZtalz+a0IEqWYjl0aHYDgoKbXG6WJZTh4P7MyhODAUgHcDKjlv0Hi//VyOev74bSiGIDuuGgtTLtxMSxRFQVVceBUnW5Z8zqJ3fwJZ5e5PfkSWNXg9bnatWMrKbz6nqqj546IDhScmc/0Lrx/NW9Duen3Qq9m6x4c/TnxgPCW2EvZU7aHOVUdcYBxJgUlEB0QTHxBPkCHI7xiP4uHdze+yOGc126vXNa6v2/kEkQEBPHlBTyb3OLJATVVV5m0v4elftuMpyCBAtfHX6DUM6ZZC9PlPQAsJy4IgnFnE45jTQF1lOYW7dhCekExYfPvM+Nrg8XLBhj1sqbf7rX8izMjW6lp22d3koKFK3zTCx+hy8rxaxWVnn93iOXN3LyAj/xYAVHsCE85ZeMhenw0L32PB2z8AoA90ERwVS0O5l4bqqhb373vWVELjEijPzWHgtAsIDIs4ZWuC3DT3JtYUr/Fbd2mXSzFrzbgVNw6vA42kYWDUQCYmTUSvafl13rv4XuZmz/VfWd+LGQOf4qohKUfVNpdH4cMV2eh++ycjnOsxyF7iOyUhTXkGkkce1TkFQTg9iCDkNJK7dTPGgIB2yxWpdnv4tKiSn8uqWV/b+oyrge464tyF9N2dyZSETpx1yeWt7rvgt/GoBt+Mr57aeAYOf5uwSF+Rsq9WzKO2ditRVi0ajZH0mJH8+tQ/cFQ1/4TdbdQ4rD0XY3dvx+vQ0LXnXaSk3HGMr7jjUFUVh9fB17u/ZvbW2X6PYw4WFxBH55DOBOgCuKTLJQyIGgD4ckqGf+b/2Ku3ZgYfX33lMT/yW/L714xafjMANo+OApuVEL2d8L7jYfITENox8psEQTi5RBByminJ3IOs0RCRdHSfWo+XC2Z/zrq4VLSKl16Kk2S5nlTdeySRTSC+8uANDUEEcw0jzvk7Gk3rqUa/fzcZTdDexuX8+k7Y3DJdQvY027fecDfauu9wl5birNFjip7EORc8jkarYcHCpoRYgyGGkSOWHsdX3HG4FTeL8haxs3InLq8LSZJQFIUSWwmri1dT6fDP4UkMTCTIEMSW8i2N67rYX2ZdtpNXLu/LBf3ijrlNa168kEH1vgkC3XeuR7fqDSqXf0aFw0S0xU7gyJtg9D/BFHzM1xIE4dQhgpDTUNGeXcgaLVEpqYff+QTYvXs3n376KQAXXHABffr08gsAALweGY1WAaChIJyzL1uA3mBp8XyKovDRUyOJG1aC1EIaQaUjlFBjy3Upvtx1PueOuJdpvWOpqPiTjZtuJDLyHJKTbicwsMcxvMpTU72rnh/2/sDqotUsyFvQbHugLpCZw2fy0fwglmSU8/LlfbiwX3wLZ2qbgkdTiZPKWal0Y+jjK30rS3fA3Acp3LqaBo+epHAJ/YT7YcCNcIigVBCE04cIQk5TRRm7sNXW0Kn/oJM+embmzJmN399yyy3ExcWxYuWt2GzzAfB69Qzo9Q2rl96GPrQQAHt+d865+ntkTctlv79euJFNHz5HUmQhhhAn9m5nM7D7BPqmjkCWZT5b8AyqfSEerKCqIEFetYavd59HlTOEnU+cjVF3apQUr3F7WFtrI1yvpavZiEaS0J2AESUV9goW5S2iyllFqDGUJGsSPcN7YtAYuHbWKpZklPPvy/pwUf9jD0I2Pz6U3soOAJaPfJ/hEy9q2pgxD2XOg+TmlKAikZQchXzWU9B54jFfVxCEjk0EIaex6pJiynOzSerV96SWfC8rK+ONN95oXJYkiZiYGJJTXkSj8ZUBHzN6I1ptIL9/dT6asK2oKrhrrMRG3UzPobe3+nhm5m1/JbA6D6X3BP754N0ALNpVyg3vr8Gi1/D21X0pzN5LcFgEs1fmsSLfN0He19d3o09aAlVVVTidTsrLy/F6vXTr1g2LpeUemONK8cKu36BwA/S8CKJ8vTCqquJxOkGWWPL151wV06fFw9PMBi6NCuXCqGASTYbDXs7uVZAAo+boRqBc97/V/Lm7jJcu7cPFA449CKkoKyb7tWkMkDPYJaeRdO8ijOYDSvJ73bBuNu4/niG7AixaF7G9hsDkpyCy4xXnEwTh+BBByGnO6/GQu2UjkiyT1Ksv0kkaFpmdnc2cOXMoLi5uXBcUVELvPr/77dc9/RV2rv0Cj2EVss73eMZVHUZc6L8IiehCfPdefj05X3z+A/nfvUeDxsyGoXeQEh3Mt+sPPfQWYIxuLyma5o9szGYzd95554kNRJ6IBK+zcdEjGfjGfiH60DiKdu/EXlcLgIrEi7c/cdjTjQkJZFK4lWi9jkCtht31dlaV17GnwcFOj7txP5Mss3JoN6IMukOez1tbi6TXIxuNqC4XzqxsXn33F3YW1XHO9Ou5ZPDxmUAxc+cGYj6bjEly+VbMrGm+k70K/nyR+qX/o6jBRJjRQeiIK2HsA2AJOy7tEASh4xBByBnCabORs2UDBpOFmC5d0RtNJ+W6tbW1/Pzzz2i1Wrp3705ISCa7dt+Hqroa9wkNGYnDUYTN3pR8Wl9oZs9PSaSPGMOkW+5Eb/KV/v7zsw9Z8/2XAHwfPY08U/MhyTIKZsmFVasSFmDAgJtJ2QuJz97Ltp49sFksJCUlkZPjG3VzzTXXkJaWdmLeAFslPN88SfiP4lQ2VcViDHGQOK4IrdFDdaaVFzs/TIElkQDVjiqpNHBsJc9XDu1G8gE9J3V2B56NG1G2bcW+ZSuurCycu3cDIBmNqA6H3/E5dz3C2X+98pjacKClHz7KyMxXAFjb9ykGXvC3lnes2AvzHqF84wIqXSbiQ1TME+6BwbeC9vA9QYIgnBpEEHKG8bhcFGXsxOWwYw2PbJdRNE5XOStWjMPrbX0Yr7vezPZPU1BVBWtEJJNu/T/mvfs6tWW+8qjaniORhpzP639m4/L4elCirAaeOjuBJT98hiTBxIkT6ZKQxg9PPMTIpb7J8IL//nei/3o7kiTx2GOPoaoqF110Eb17927Whl0NDq7bnMmY0ECe73pk9VecWTU0rC1BDtBhTA3G0DkY6b8ToGCd336/F6URfsGj1DALxbC1cf273MFiaULjsgk7RlxU4V9U7FCivBKpoWbW19lwKL5/qmE6LRXupgkQf73rBkwuZ8sn0Gph32SJJfFpjPj2U3TW1mczbgu7w4np2UgAloddzPD/+9+hD8j6E3XOAxRk5eD0akmOD0Jz9hOQPs0366EgCKc0Ubb9DKPV60no4bvhVhbmk7FmBRGJKSe1XLlBH87YMVtQVZUG2x5KS3/DbsshMLAHBmMMHnc1YWFj6JFazpy3/kNtWQnfPOVfJvyuh+8H4P8mpbMhrxqHy8vglFC0Gpl180zY7Xays7JZ8f33TFvaNBuvTqNpfLwTGBhIbW0tOl3zxxXraxs4Z10GAB8WVnB3chQxhpYLfHlqnNQtzANFpWF10+On+sX5SCYtYVd/g2fXVvTSHgp3/khZ1m5MI26h/5TzKC60sG3nrY3HXFSxiPhAN87QbvQINHFhbC8iAyN5bOkLvONtXtTtysI1pBb3Qlfj5t+DzdRYNJRoVEpqGvz2OzAAAQju15eA3r0x9uiOecgQJI0Gb3U1cmAgmoAAfr7oeqzlOaxKSea1/35BVEpnZp49jEjToYu5uTxeduyuxO7yEh1uJjne/4/K7j+/YH/WS9SYmw55LgBSRiPdtpj4jZ/i/eMJsvNr0P/vTuK7vY005WloJYdGEITTl+gJOc1sWzyflL4DMAcFt3dTmnE57Cz9/EM2zvkFVfX1eNwx6zNMAa1/Ml+0aBGLFi1Cp8hc9OVnftsi/3kvYTf7imb997//JT8/nyuuuIL0dP/kx4cy8vlvfnnj8r/TE7gqpuWchPz7lzRbp0+y4sqpbbWN2cm7GHn7XwDYuvQtSlwvAiDX69DZdKgeK7XEoY/ciqz19VjUYqWEaDxoCaSGQmcfFjmvZlNQ86TjEcEB3BAXzrDgAPIdLrblFfCP0qbApHhc32bHlBQW8cuaTWxyuPkisnnPj+RRGCzrebZfMt1CAxrXK4rCD79lsmNJIUHV/sGOaXA4N93Um7yMzRT88SZDSw74ebSUE3IozjpY+gq2JW9TUGsg1GAnbMhFMOFhCDw15/wRhDOd6AkR6DFmAjuWLSZ1wOCTlitypPRGE+NvuI1hF19Jwc7tGMzmQwYgAN26dWPRokWoB/XW6xISsIxsKhN+qFj63IhgvyBkSWVdq0GIJsyIt6IplyL+2VEAFD65EqV+X6KoVgJP0/WsuyP45NLziO3UmTppLxbf3H0oAW6cAW7AhpGmXhWAuKJ0+mZcBqrMvCgj/+kWBQfEH4O1eu7qHs+EMP9/yOF6Lb3SO/GP0qZiZLnZOcQnJiDLMna7nfu/m8u3EXG4rdFwwOFmp4NAj4cKnQmPXsMqPIzbkEGaW+I6i5WaZSWouTYsitTiA6OKtWVwE9g+uYah5DSuXxV2PkNafDcPwRAIEx7GPOAGOv8xk4p1v7D7z9+I3/QT5rH/B8P/BrqO9fsrCMLxJ4KQ01D6sFHsWrGE9BFjDr9zOzAFWkkbNPSI9o2KiqJHjx5s27aN2g9m0y8uDn1868NMDxx94/Aq/FlVx+3bc/z2+bOqHkVVkVvIQ4j55yCcmdWUvbsFNJKvhHq5HVuUBWN9te84/w4CdtWXUCwrFGfvAiDgx0SCnA6MBqiRNOgThxNoqSbF3gm9LRJ9QywajwVFVdhkqOGRblGN53rJEMRlgxPRaVuvg1LX4J9/MzirimuWb+D5S6dy51e/8muCr7Bd59IiRjjrSbQGMG1gHxLjfbMduzxenlmXw1u2WpAl9hjgEU8t1zW4SVIkPKi4Es0MHpfAsP4xzFmQTd4POZgViafu+5q79dWwr3mLez7FsPNu5agFJ8AlswgbsobQOTMoyNhF0Y9vkrxmNprJM6HXJSJfRBBOYyIIOQ1JsozWcPJqiZxIilehPsuApTaVDb8UsstSDdJWopKCiEoOJjTCislkpmqXBouazJ5lNVTvysTu8PBDcRU/p+qwmX1DmW+Nj+Djogoq3B52NDjoEdDyJ21d9L4hvl6Vnc+sxlLjxLjvRrg/cKnTa9hVU0OB00CSzknX4AjyiguwGXTUF5qpl3zn6JFfRtifa6kZcAdBoVE4vXZya7dRWLGGEmr4980P+l3b7SlAp205wbigNget1kqUNYTPwvRcWdE0KunLiDhq3v+SXzv3QPZ6+Y+7mksuO7tZcbt6p5t/LNjNLzoXaPy3OQM0JA2PY/y4RMzGptyawQOj2fJHLsENKsE1oXzEO1wafi/5k55gzJiLOC4SBiH9ZR7xW7/BO/dRsots6D/6B/Gd3/ZNjpcw+PhcRxCEDkXkhJymtv+5gK7DR6HRHrquREeXtbmcX9/cfNTHF4RqcF/fiVviI+hiMXL1pkzmV9bySGosdyRGtnzMljLUT3b6rWuQwB6to4oi8vIzqCjcjOJ1ABKyJgbFk9m0s6oyILuYqFobXlkmM7UT1UHBNATHUCnVYSjLR9r3z86uN/L6TQ/5XesfEcW4FDBrVG5KG83i4m28lb2XDUq3xn1C1AqqpKZHSkmFeeTE+nI/HlXq+euElme0nfLjRjbsewJmcikMUHRckxDO5C4RmPWtfyZRFIVf5maS+0MuAL27rGHUPfe1uv8xcdthxevYFr5GQY3Wly/S/2yY9BgEJ56YawqCcMzEEF2hkdfjIXfrJiTA5XQQHBXTbjPyHouV3+9l3ZymxynmOBcNxRKSRgWvxve1jyp76T06EVTYsthX9MwxNpJ/XNETAK+iMHTxNvLw0l/V8XnvTljDm/eGlOfVUvvaRvSyxE57EUVyHg5PGbVlu+GAmihNZEAhTmciNimF7hdeQtk111MQG8vGfn2pD/TPe9FWlzPA6aDTgMGkXnEVql7DhUs/YK10ZI+o9tMpbi4umcfNhd8yO+Z8fg4ax5uRFiYM7tfi/kuzKrgkOw+Auwng3tGd0LShCqstP5P3n8wGwCjXcfWDvTHGncBh4XXFsOAJKlZ+S4XTRHygA/Oo22DUPb6cEkEQOhQRhAgtUlWV3SuXERafQHjC8amYebL89NpGcrf5qqNe/tAgwuP9bz5lRVWsXbATl9PDOdcMQ7fv0/xTM5cRXOzEemEi156VhsvjZdTcLeTsqxcmKSozvq7i4un9iOsa4ndOW62LT+5bSn3NJyjeEr9tsjaIgNBYolLSCAgJZ/2c95EAKSKGnuMm09BQT21tHUU5eTj2VW7Vut10rqhgR3TTqI8D5+T57bsByEF1LGUs+SRQb0hniatz0zXxMtWYwXD7O+ys74otN5bLPQvoX7sds+IbbTM/ZAhx575HeqeWf76b8ms4b3smTp1ElE1l4zl92zwPkaqqLHvlczbt8uWwWLTV3PD6cXoccyhFm1DnPEjBjk2++iKRWjQTH4J+14B8aswhJAhnAjE6RmiRy26jrryUpN5927spbbY/AAEIjmxecTQiJoQpVw/zW7cjrwZ9ta/HIiBAj9urMPWHTeSE+j71Gzwqo7fY0ajw/csbeOzLa/jX/f/g3n/9AwBTgI6nf/grnUNUzurpmzE4ddCFJPRIp9/kYdgqq6hYm4Uzuxo1fAJadHi8Tr7MKSWmtszXiH0BSLDLybk330JKchKPP/EEFkslFks1G1f9RFr6IFatG4F+31CUscxn3JidyBodXkXhm+zlFFfuJXDLLOKii1B1EuFr9Jyr8R+qDDDgmv8SHOb/qEJVVcrrnLy5IptPFBtOo0x8ncK7Azod1USIkiTR75qz2PTwRgA0kufQBxwvMX2QbviJ+J2/4J37MNl51eg/f5j4Ve/56oukjD457RAE4bgTPSFniOriIhz1dUSndWnvphyxjLUl/P7fbY3LQREmVABVRVUBFVRUJCSs4UZ2mFQqixpILvXdHJ1aOP+JIby2NJvPA3zDa1NrFBaf1xdbpZOPHloBwN/emYAsy7z00ktMnz4d8N1wZUliQre0xkAkWDIzOv5GTNqmuhoH+jzKzpcJCja9EZvegFOrx6vRoPV6eMG9EKv6EUajr77H/n91B8YCYcZb6TPsX2SVr2Rtxqtg20KoZG/cx1GtY+rmIr9r7rKmE3TN50RHNj0WcXkVhn23noKw5p8x/uiaQs/YI6/YerAdX3zP0kVWuhllonUQ0b2UoBsu2/ea1BM/y7PHBavfxTb/JQqqJF++SN+JMPlJCDm1evkE4XQjekKEVgVHx7D9zx2EJSSiOwEjZ5KTk5k+fXrjTRygb9++XHDBBX6PHtoiIMS/nTVl9lb3rat0YMVXFkORoDhYw4p0I89vzIADYoaF0/qglWWs4SbC4ixUFDS0dkpAomfICC5PaV4NtMFTg83cgDbQQKlLomtdKOeVm5jVzUKVQUajeriJt+mvrqVGDiLSkOd/5oPu1UbnvRTVZ5D94zACA8sIA1+qCVBfZKJkQzh1+WZqO6dypWYpAFXaQKJv+ZWgQP+aJw63l8qA5rkeaXXqUQcgHoeHohVFeIvjmRLU9HOo2xmDbcZXeNVoZKmOoL41WC4/fvPTNKPVw/C/Ye5zJZ0XPU3Fss/YvWoZ8TuGYx5zJ4yYDvpjm6NHEISTRwQhZ5Dg6Bg8bvcJCUJOhJjUIK5/ZgRFe6qxBBt8N25J2vd/kHz/wVHvpjCjmpJyG1/a6nD2DWaZ0jyB9L6AEPQH1N/QaI9uFmIlopIu95zX+Knfqyj8+dxKOtd4uSPLRdCoEKqq3qSnewEAVmpRVYna2p50TruMHj0uY9n8K3Fp1jee02F4Eb0B9ICqaCivjCOg80D6Jl3N+z89gbnQSWlYNC+M/xv3qAoGxc2HyZGMDmxedM2olTEqcHDI9kBq7FG93oyvM5DWFmMEglvY7lV9uS6KGkjVhkBsuz7G1M2CIT0RbbfeSCdihJYlDKa+RNiAGwn97X4Kdmyi6Of3SF73CZqzH4ceF4n6IoJwCmhTEPLWW2/x1ltvkZ2dDUCPHj145JFHmDJlCgDFxcX885//ZN68edTV1dG1a1cefPBBLr744uPecKHtbLW1xHY5tUYVBIQY6Dwo6rD7JfX03Ywv2Le8KruS8zNzQJIwOxTui47gtn7+5cstwQbIqTuyhrh3oAm2EjCqM4FjR/lt0sgy0sRE+CaLc/LsSIEhZJX+DECl5QKSpDhi40YSFzuoMXAZNekr5s3tiayzo6rgqovGYwulvqgXtTlDqNE7kbPd7M7Po8+0W/k2bxVzOp0HgN5tY/7grqSGRDRrZkZtCRf+mUOVxTcvzPWKiWfHdzmixyT2Gid7P96Bp8yOYveg18toXQoB+w51qWAza9HFBBA1Pp6gJCu2pRtwbMzBW2vHZfc9DnHaknCuA9bZkKU56APKCTxnAIZ+zScWPGbRPX35Itt/wDvnIbILG9B/cDfx3WYhnfMcRPc6/tcUBOG4aVMQEh8fz7PPPkvnzp1RVZUPPviA888/nw0bNtCjRw+uu+46qqur+fHHHwkPD+fTTz/lsssuY+3atfTr1/KwQeHkqCktJjAsvL2bcdIMSQ7lgec24pVB74URV0VRmlOLNdxEaXYtlmADGl1TT4iqqjgafHkjtbX+88SY+0firQ/BlV2LbZsGSV+IIS0YxeZGcXhx5dTSLbMaN5A98S+wt+nY2NjzSNdFUV+2m7z8z3A6SkCWCQrrj6zz9VVIEiStfR6Dy4sK7ELBW69Ave8cq/fsZk638xrP6dQH8PaeFQyXE1n9+xyqrTHkBQazjd3YEybBvgBkyC4Hz93e94jer5pdlZS/v62pp0MC3ApIvvemLi6QLrf0Qmvy/5NhGTcQy7iBACgNDRQ+sd5vu6IG46gLxvFFDdY/niLw3geOf96IJEGPC9B0nkzq8lexLXqNPVt3ErrnLMKGXw7jHvL1nAiC0OG0KQg599xz/Zafeuop3nrrLVauXEmPHj1Yvnw5b731FoMH+6obPvTQQ7z88susW7dOBCHtrDQrk85Dhp+w88uyjKqqfrkhbrfvpn6suSFH6+ybujNv1nYAFn+665D7qqrKiuUr2bJlC3fffTcAyr5J9mzrSxv3c+fVUZ3Xcu+Jommes+LZfC9rTJX+K71w0FQyBKp2ZNkAQF+zhk36QoLro9FbCwlJ/wNoqiESX1mKZfF6ckN+YkTvNUiSQnZWP7bmXYpGrcebGMCFy+vpmddSTZMW2u1RqHt/G4Z9yzUxFiSNjCHUgD7CTFD3MBLiWk7GPZBssRB7Xzfqf1uJaXBXtPHx2H6bS8PqMlxqOrUVo/G8/Dmh95ygnBG9Gcbej7nvVXT+/WEqNsxl94IfiN/4A+ZJ98PAm0AjnkALQkdy1P8ivV4vX331FQ0NDQwb5hsiOXz4cL744gumTp1KcHAwX375JQ6Hg7Fjxx6v9gpHoXD3TqI7d6EiP4/ashJfXgUQGpeANaLlqqFtFRERQVFR08iN2tpasrKyAFBVD5lZ/2H+go8IDOyJqipoZAMxMRcTG3sZknRiaj10GRRNSJSF7csKqSpqoDCjmoPHgoVEmwkJCqeqppwff/2W3//4GbfX2+xc2ggTslGLLtqCbWs5KCqyRYekkfCU2TH3iyQrSs9b9c9ysfnfROILXDz7AhDZbUHRNaBzhKOioGgcKBonyF5QZOo0TvTxEZj2zdY7KdhO3Tm3AKAgcZf6AnOYyi66MW3LciTJS89eCxrbl95tKf2dXdmbO4jr1roxulW0nXxdKfYGFxqthN7Qcm5G5s+ZjXPnuUbH0+Ocoy9AJoeEY71qWuOy5cILsFygkj9jEaDFVhpP6FGf/QgFJ8JlHxA2aAmhv91PflYeRV8+Q/Ka99Gc8yx06phzKgnCmajNQciWLVsYNmwYDoeDgIAAvvvuO7p37w7Al19+yeWXX05YWBharRaz2cx3331HWlpaq+dzOp04nc7G5YO7woVjU1lYQHluNsaAQPJ3bKXPJF/+jqooVBTksXfdKoyWQOLSux/TdcaPH8/s2bMBKCws5Prrr0ej8QUXqtpUT6Kubmvj9zW1Gygo/Iw+vWdhMDTPbzgeIhIDGZPYFYD6Kif11Q4iEwNpqHGh1cmYAvX0u/xP+vfvj8PhwOFyoddq8QI6nQ5z30hiHxuObGgKlEIu7tzitTZX1rJ0U2eWqm/ySsgKIqpeAuBh9+vcE9qDSwb51/Hwuhxkr34fY0AScY+fA0D+/UsAiLYlUe/Vo2pcyKgMZiUDWM2nXAeojBz1qf+5FJlSWziXFkkYFV+k5ckM4I3bF/jtp2rcSEY3uiA37koDwW4TYwKb/gykTElu2xt8JCSJoO4F1Gw/yUNoU0Yh3baYhPWz8fzxFFlZxZjfuZrY/uPEkF5B6CDaPDyga9eubNy4kVWrVvHXv/6V66+/nu3bfV3eDz/8MNXV1fzxxx+sXbuWe+65h8suu4wtW7a0er5nnnmGoKCgxq+EhIRW9xXaLigykoSevXHU1xGR1PQJV5JlwhOSSB0whMDwCHatWILSQg/AkZoxYwZjxoyhpKSEd996i2ljx5Ka6pvN1eksabZ/UqJv5tW6um2Ul88/6uu2RUCIgeiUIGSNTGCoEVOgL3eiW7du2O12PB4Pv/8xn8qqKlRVxeVy8eTrz/kFIIeSbNr3QEOSuKLfHYSl/MS7jmfJ1Mfwt/pK/v7rFmy2pkckGr2RxOF/IbTzGDJzd1NeWYpXu2+iPMVAzJZbYV/PjdcRwN4dw+m8op5Aa5nfdR0uPXPXXcjl+amEKIf+Jy15ddBgxl0YBA6jXwBS4TlxdT7qdjblZKiek1TkDHyPXwb9Be1d60gbfzGBOjcZq5ZR8/JIWPg0uGyHP4cgCCfMMRcrmzhxIqmpqfzrX/8iLS2NrVu30qNHD7/taWlpvP322y0e31JPSEJCgihWdpJ5XC52LF1E5yHDMVoO//y/NSkJCdx67rn8Y8YM9AkJ1NRsoE+fIYweY+H665s64mXZgLKv5PjwYYswmY5P8OlwFpOd/SbF9noWNgQTGXcTZ0WGk2Y+8cOSi5wu+i3fjlaC/LF9AXC6PTyzOIN3ZAeqJJHeoPJut0S6pIbx4/bveXDNw43Hh3mDmRHxND2WNZ3ThpM/DBsoleoBCZ2qwa26iI7bS0LCVor+jGPonCycsoXIr35h1e87ueCWUZQXVrNp2V5MVh1BoRb2rC+hNLMBl8PjG9psthMeaGC0LbjxWlGPD0WnPzETHtbM+oa6DN+jP1mqxRRTTdB1ZyEHn+Rk6eKtMOd+Snasp9ZtJCnGjH7KY2JIryAcB+1SrExRFJxOJzab7xOFLPt/EtNoNCiK0urxBoMBg8HQ6nbh5NDq9fQcO5GC3Tvwutwk9e7L/vi0LZ+OI2NjqQoIQL+vR2v1mr9TXNz8k+/+AMSrHchHb4/l/aTbuCZxMLf2H4NObvvNYO+61WyY8xPV1StRcaG3uknVKCyt38OrQ25g1/nj23zOttLue588alP1UINOy8yJ3Ri+vYi/5xez0yIxJTOXR/Or+KX4E7/jKzTV3Ft5Bw8F3sLwur6s1mawResrcqZVNZwzdDJhRjuf//YbxYVdMKwLZ9i6jRg7XQSJfah7fwuddV42PjcXfYSFfiNSiewRD0CfYb6qrx6Xh8rthRQv2INa65+46usJO7IgxOv2sPCL7wnbbSDCFUxGWAEDbjkba3Bwi/tbr78A+bMvqNmWgKJaaSi00vDsVnSGEiLuGo8cemIexzUT3ROu/4mo7T8QOfchcgrrYPY9JHUXQ3oFoT20KQiZMWMGU6ZMITExkbq6Oj799FMWLVrE3LlzSU9PJy0tjdtuu40XX3yRsLAwvv/+e+bNm8fPP/98otovHEeSLBOf3oPq4iIy169B8XrR6HR4nE4iU1KxRkS2GpA49lRTPnsro3oMYfb7HzCox2iCQ4J59T0TcgtPCOLib+DeVT+xp0pGF2qGuo/IWvcdCXVvkTemT5sDkWWff0hZbjZg2Pfl05VKuu79N+X9U0/45H36A94bt6r6LU/uHsMfccHctmI36wzwL6WeMaZ7UZx/R1b9HwksC8nEHtmL7IKmKqseSWHD9++TFxNDgCmUC129CQsNhEmXAvtesWffF0AduDKz2Bizi643D8MUYCZvdQaO73IxqXpC0bE/4KjQ1xN6bmcMpiPrLXK73fz29kf0L2jKjelcEUfeiyuo09kI9JoJcgVQMUmm97jhSLKEpNUQeO1VWCpLqHhjHm5bMIoahNsZS9X78wn7xxVH/D4fs31DeqUuZ5G87FWci19lz9adBGecTcSIy8SQXkE4idr0OObmm29m/vz5FBUVERQURO/evbnvvvuYNGkSABkZGdx///0sXbqU+vp60tLSuPfee7n22muPuEFi7piOqTQ7k+qSIgwmC/Hde6LRNsWvqqpSOHMFqtOLPdDLP5b+h8XLF2A2BDBz5mO89e5rjBvfidtu7UF09AVYrb1xKQpp766ka+BGSpVZjecqS/yILpoSvhg8jhij/ojbt2Xh7/z+9quNy0qYA7nC/6Z665uzT2itlAavl9Q/fflPe0f3wqJpnkviURT+vWwv/3HV45Ulou0KjupH0LlzSHBGERqSxhtTX8LWAB+8+x4OdzUKEjJN/0ynuPoRpxzbGJM62UFpSANxQ9LoNKI7subw6WG1ddVsfPt3OlXENK4rNVZRPQhCVkOEM6TF4/Z2KWfAReMJCG4qGe8tzqfoFd/oKUNAPhEPncBS74dTnQfzHqZqw2+UOSzEhUhYJt8nhvQKQhsdzf1bTGAntInb4WDv+tXEdumGNbypC92RUUX5rK2EXdMNfXooe9aV0lDtJLZzMNGd/Ocr2T8CpEYL76UZ+DN4BSmVX7A56k7qzE35RE8bg7hp2JENF1UUL9sXL2Du2/8BoEtqV7Ky9/gNt73qqRcJS4xkw8brqK/fQXrXp4iLO36fwF2KQuLizQDsGtmTIF3zG5iqqryeW8q/s4qx7/unl17j5bGtDlL+3o9Ea/N5T6pdbl7+7ie8u7aiVRT+4pjQuK3WrGK1ta3XSBodTuzkrkhHWLZeVVVKqopYP2suvSt8I93qNA2UpNkYeuUUjEYTDbV1rPx1Hkq+nSCnmdi6FoK9W+KIT+0EgLe8jKIXd/rag524Zye36TWcENlL4bf7yM/KxenVkpwSLYb0CkIbiCBEOGl2r1pG58HDW308U7inmphOQUgHPVZx5tRS9tYmv3UDzzqglLyqElFXjdHjYkKJhYduHkqAofVPo4rDQdVnnzN7zteHbK8rIop+Fy5Fd9D08336zCI8bOwhjz1SqqoSs8j32mb3TKHc7WF9bQOLK+uo83pRVJAlqPX450il1XnZE6jBopG5OymKWxMi0LfwDMvtVVi+N4u42bkYlebvSeD4BAKGxWLfWo59ewWKw4u7lcJqcoCOgBFxWPpHIlv1eCscuKvt5KvFREXHYjSZ2bV9MxULMgkvDyDY0/QzKjSV0/+Bc9G2EGTt53I6WfrDryRtDMKg6FAVD578NVTG6xjw/N04liyh/BffvqbwfMLubceekAN5PbBvSG9OOZg0bmIHiCG9gnAkRBAinDT1lRU0VFcR1an1GjAtURWVggeW+q3zAk/1MPBjvJ6rV84l0OmrPGrUhDL6iisZ3rn1pMVdAwdR73SwqLv/DSIMDcFhEWzVG/GaAxg58SsAcmrjeXfr5SiSmwcGzKJn52tJS/1nm17DoUQv3HhE+10SFUJXi5FOJgPxJj0P7M5nXa0vNyTVZOCZLvGMDm19nh+3R2HptiJ+2lVCkeJlRZiW53omcXlMy49pvA1uHLsqcRc3YNtQhlLXlJQq6TWoLv/h2TWaOoK8/tf3SB72JpYy+MqzCGwlAbUl37z+GD1nL0Kp95WJNfS+HH0nX2+OBweGs+sIGjIes+nIz3nC2Sph0TPULf+IIpuZqAA3QWNvh5F3i1l6BaEVIggRTqqsDWtJ6TfwqI5VvSpl72zCldv0Sd2LyvvGpuJaKZYuXHX35ei0rdfpyLvtduoXLyY7zEp53x5MvPomIgcNRqPT41VVHnrkewI8OQyd9BoAH2T3ZYNmNwByXQ/eOfc63q5NZ2RIIH+JP/YRGpPW7GJLvZ0Yg440s4FeAWZ6B5oI1mnQSRIORSXeqKerxT9fRVFVviqu4snMQspcvt6aCyKDeSwtjqiDKp3uqLfz+N5CFlb693J0MRv5c0j6YduoelVsm8uoX1qAu7AeWvkL4JLc7A7MJXhkIl1SexASHYmkOfJHP5WVhSx65Fa6zt+LfNA1VvYPJ39gCvOD1lOxrzZKlFelm8bCPaOeJqXThBbO2A5KtsOc+yjZvp5aj4HkaDM6MaRXEFokghDhpMpcv4ZO/Qcd9fH2HRVUfLDdb12BXMmKkL1ced3VREUdevbcygYXe7ftRXfDZegUL3k9w5j42Xy0uqaE1v0VQ2MGzyIoeSXvFo1ku8c3yZriCqEq6AmUiCAi9Vo2Du+BfIw3lgavlzqPQnQrJdIPp9bj5fmsIv6XX44CBGpk7usUw41x4chAmcvDf3JKmFVQ3uzYj3qlMCk8qNn6Q1FcXjzldur1Di76/gJ0qpZYVySKpLDNtBe37GHjtRvRyG0rrb/k6/8gvfguYdW+R0/5w1MZfN+LFJ5/IQA2Pdzwj5Yf54R4Fe5Jmsr5455Famlo1cmmqrDjR5Q5viG9GkkhoVtPpCnPQswJmBlYEE5RIggRTqqsDWtJ7jvgmKps2rdXULc4H1dOU7n+uKdGNvvErShO7PY8ioq+wWrtQ3j4BDo98Hvj9nBbNe/Ofx7z7BdIH9SU5Hhw2fKyNA1fRj+GxluGhmDKeAZvoq842/jQQF7tlkS4XkteXh5Op/OQUw6cSJvrbNy3K58Ndb5HNL0CTBS73I29JPv1CjDx357JRBt0GI7DDXtj6Uau/c1/NNsLY16gb0Rfoi3Rhz2+rGAPK++/jbQ1hQBUBWsJeOheek29jm9f+ivd/7sYgIW9JPZMDSTGEs3fpv4PV0Mpu4vX8cK6l9kh+R4V3R81hqvPfv2YX9Nx47bD8tewL/wP+bUGQo0OMUuvIBxABCHCSVW8NwNreATmoOBjPpeqqriLbWiD9Mjm5r0IGzZcR2WVr5RohUfixRITpdue9dvnf78/TcFFyVw940PkfcNj5513IRHZRay+/V+E9RhATpWNn6nGY6sksMHEk5cMZrfDxX2783AoKhE6DVdX5uFYv7rxvNOmTaN///7NCvGdaF5V5ePCCp7OLKLG03pJ/d2jemE9xCOrtnhv83u8uuHVFrctvGwh4aaWhzgrisKyWU9hfPMzAuwqigR5Z/cm4fLryNu+GvfSlaQtzwUge1J3Jr/8ORpt85+z01XPs99cxNeuIhLdbj45/1uCI45tXqPjrjoP5j1C+fo5VDlNJIQqGCfNgIE3iyG9whlNBCHCSZW/cxuRyZ3QG00n7BqK4sZuz2PlqkmN66bn+RID0/L0jPmzB3uD40mtKeCsHF/gkNstjEnf/Iksy3x1zvn0zNzN4gce5fbrWh+Ou63OxoS1+3JFFC9Xr/odi8vpt88jjzxy0gMRgDKXmyf2FvJlcVXjOrNGxuZVODvcyuxenY7bteweO1/s/ILfc36nsL6QCkeF3/Yt1zefB6okYzOb/nUHCTt8+xbHGIh+4nHCuvQic8pUghqa/sQUntOfMc/MQmtovTBadeVezvnhfOpkiVSXi+9u3IrUQsDS7rKXof56H7lZeSiqRFJKDPIUMaRXOHO1S9l24czktNko3LWD+PQeh9/5KDkcRSxbPtJvXVrqfRgK38HpdfH0xzZgDeSu8dtHX13fLFhQlNZj7S3Fpdy5YRcYfaNBop12br36ajatWc3WrU2z/i5evJhx48Yd46tquwi9jle7JXF1TBjflVZzQWQwA6wWbF5vi7VIjoVJa+KGnjdwQ88bACi3lzPuy5Zfs+J2s+yVGQR+8AsJHnBpIf/S4Uy67zX0RjMLX/gH0fsCkJz0YEyXXsSEqw8/Eik4NJVno8bxaO5WLLZY1s55mwHn3NkuAeAhJY9Aun0xSetm45r3NHv3lmB990qi+o2HyU+JIb2CcAREECIcleriQrqPPrHzsaxZe0GzdZKkZe0166hxVFD4zMhm2zNuHsfZ//di47K6L18l0dh8fiKX18sjy9fzsVPCYwxEo3i5XOPmqYlDMOl1dE5K5Oyzz+bFF33nW7x4MYWFhVx22WXodCf/k/mQ4ACGBDdNLhgkn/h/vuGmcK7pdg0f7/jYb729poLVl51D5L5cnsxUC6lPv8DUPuOoqSjiz7cfxfitL2dn9xVDOH/m7CO+Zn5VPnOyjYwp9fUo/LK2gl/WPo4ON+khXgaOmEDSwEmHOctJImtg0M3oe1xI50XPUr3sIzJWLSd223AsY++AkdNBb2nvVgpCh9XBPloIpwpLcAj1lRWH3/EY9O/3abN18fHXABBkDKPbzh1Ef/smamjTaJhekcHojU11HAL25UqEvfIi3z7yBNs2+IqJLcrOY+jvK5nt0eHRaOlkr+PH9Fj+PXYopgNmkg0ICOCGG25oXM7IyOCpp57ym/n5dBdmakq6vGfRPQBsXfI9kTm1OHSw6/ZJTPpxOel9xuH1uNl4wVnEvf0zYVW+JNouZ116RNdRFIUPF3zIG6+/gb50/41bxYDvvXajY0uVkdk/L2HlrPtQ3K7WT3aymUPhnOcJ/vt8OvfpQbVDZu8v/8X76iDY8rVvhI0gCM2InBDhqNSUFuOy24lIOrKy6kfL7a4lM+vf5Od/hKyoDAn8K/ouF4Ak+76Q8dTXkjXhssZjUn/5DkmnA1nLinffJ+LLL/zO+cI9D/JbWndUSUbvcXN7gIb7BvdB08JcL+ArsPbnl2vImv8juqhc9miTUZHbLUfkZNtavpUrf2mqaLr0nN9YfvvlJG+rpDjOzLj56xq32W21bB41FOu+xzAFV41h4iNvH/Yae4r3MOvLWRgqfT1WTqOTKVOnMLrXaBxVRexZ/hO2uhoW7KrGofqCxCipgsuuuQnFUYdsCScsuYMksKoq7PgJ75wHyS6o91VdTe8FU54TQ3qF05pITBVOqozVy9HodGh1ehJ79jmh11q9eBSDF25ucZuqwM4vY9t0vmkv/ZfOssIbg3uSGtbyxGsAXsXL4++9RsjyeryOlQCMSq/kV2kSIDF58mSGDx/epmufau5ZdA/zcuYBEF2p8sjXMuEVbgAK04KZ8POKxn09Tgcbhg0gwOarD1I6ZSBjXv6o1XN7FS/v/fIe+Rvy0SpaFBSCugVxx4V3YNI3T3h2OxqYP/tZVhY3DxgH652M6dTVN2R8/7BxSQIkkPD/v9/3HLBdwi6X4QrORdIpjZsb/0g2Lku+tZJvZBeooCqo7P9eBa+L8j3rqMnTYasyYDV5CIyIIe2GNzEHn7iJFAWhvYjEVOGk6jzYd/OtKMijsjCf0Nj4E3at+LS/wcJbW9wmydD5/GKK1gTTUGLAdxOQfD3gavMaJnvjErlDruYfU6a2ej1VVXE46gAtNXkurPsCEIB1mgr6WtaSo4th+fJPqandzdlnXX9M9VI6Mo3ku+FbG1T+/Z4XrdI0XDjun/c3fp+9YzX5N99MmK1pbpwut05v9bybMzfz2befYag3oEWLPcDO5RdeTv/U/q0eozNaOPv2J4j67lV+2FQJgISCisxqlwH7VoUhns5H+1LxauzsHfsoqscFnsPvf1iJUBsczqaNY5DtDch5LnSvvcb4s6cxYMCAM6InTRAORfSECMdMVVUy168hdcDgE3odZdcc5M8ub1oeejvqxJm+rhAUUBUktMiS7PskqiqoqgKKAl4vm1Yu5sfF61E0MqkJBaSkJxMePoS0tPFIkkRNbSFB1lgkSeL33y9Ap98GwLPbejJt3XA89iV4JYXSURlckOLGaWz6NG4ypTB82B8n9PW3h4YVKyh77z225a1Fb3MTXwEuDWT1iWDws28Tm+h7BFKas4tt115GdKkvTyNvTBdGv/opRkPzpEyny8lb379F5fZKZGQ8kofovtHcPu12tG2os1GRsQ5F8RKa2p8nnnwSALNq4EZD2kGl6JsWVBUUycO2YQ+jtYcRVDgSj6EKt7ESRWfDZSlE0ToACHE2zZ0jHXAa6aDTSn7/lXDIXuoM9Y3HlhV2geIeKPYqKuRIqivtoNEQ0akzU6dOJSEh4YhfsyB0ZOJxjNBu9q5bTWKP3uiMrdd/OC42fwVr3oO8Vb7lEXfBpMeP7FhV5T+PTSeu11pCQoobV7vdJiTJg1brbvEwrwprGzRUeyVS9V5iiUMrg8dbi9ncVOm1W7fniI255KhfWke0d9o0XHv2+q2Th/Sn6wefNC5/csMo+q/0lZGvCZSJ+d+7JPUa0eL5VmxfwU8//oTe4UsmtofYueGSG+gW1+2o27j90xV8uXsuAIMTenPOzRcdcv/dm54hr+K/jctutGSShoJMOGVEUIbR3okRU+e1uS319RUsWnweJpPv98vpSGDKlIWNvWRer5e1a9eyYO4cvNWVeI0m+gweysSJEwkICDjUqQWhwxNBiNBuvB4Pe9aspOuw5sNmT4iZB8yRMvEx31DII5C55Et22x5Fpzv8yAqt5hL69fs7NTU5eL0ujMZQoqP9Ewuzc+axd+/tjcsTxu89+DSnLNXtZmcv3+s1DxxI2G234ikrp+b775EtFkKuuZo11ZuJ/kdThVXdp2+Q1r/50G2ny8nzLz6D96C3PUAjkZaUxKizziYs6vBl4Vuy5IGvma/31XO5ePg0ek0+9KSK9fV7WbW6qbT/1dI3fts/Mm1kdPepGKxtm9CwtHQ7a9beiNFYjsejJznpdbp2bXkivvr6eubPn8+mFcuQXU40oRGMnzSJgQMHtpogLQgdnQhChHZVlpuNVq8nJLptSaJHpaEcProQivclq17wFvS96ogOrSnZzNptvonUwuoDcERdh+L1kpAwjhUrPsTt2UXnzpcyeNAtrZ5j6potrC92gEfFEODlqpJS7tgaCkDcE8ORdKf+jSTr0stwbGmqkKqNjUFCwl1Y2OL+xc//jXHn3dnitifvuAFPZHKr15K9HsYNH8qIs85pc55E4fYc3v3y/cblkckDSOycTJcRvVo9xlFdyq4vXqAuZA+XRDzqt+3DVBuTEw+dbFxevheDwYLbXUdu7ipKyzaiqr+h1Tpwu0107fIKKSkTD9v2vLw8fvnlF8r27AJJIiwljalTp5KUJAqdCaceEYQI7aqyMB9ZoyX4KD/RtpmqwryHYflroDXCHSsg9PAlzCuyvmBj1gMAGJ0wYkrbey9iP16OfquvjLo30ojaJ5Slf9Sj3fevKWhaJ1BVZIMWc79IJN2pl4BoW7eOnKuvaXGbo0sixt25jcua2S/TZejZLe6bk7uLLx58EEdsMoopAI3iQVJVgo0GXLKGepsdZV8uiKR4mTZxAgNGjz3idlYVVPDme2/hPiCTVFIlJqWPZPiVLfdEALiKG6hbmMfmvRVcOcKXu9JbXc97sdEkpZ/T6nHLlj+NwzGrxW12exSDB71PZGTXI26/oiisX7+e+XPn4q4oRdEb6Tl4CJMmTSIwMPDwJxCEDkIEIUK7yt64jsTefZHbOO37MVEUeHzfENsx98G4Bw65++plE6hzZjcuhzmD6TtlXesHtCL5/l8av1d1MjcmWfjLntZHx8TMGIwmqHnV1o5OcTpx5+biLizEW1+PardjGjCAXQF1zJh9FVctVND2782lj3/aag/G/JXfsfFl3017yjOP072T/+iX+toaPp31Xwpr6hrXPfroo20abVRbVMX6BavYkLmFGm9D4/obzr2K5AFdjugcm36+m3Lzj5gdXRhy9i+tvp7ffrsMvcH3O6MoMm5XCBptPFZrb/r0vhuzOajF4w7HZrMxf/58Nixfiuy0IweHM3biRIYMGSIe0QinhKO5f596H8+EDsvr9Z7cAAQg+8+m75NaToY80IEBSCftcHpNXNymy9mL97DtFv+chwe0Zm7aXEnso0Mw9Wk5j6DomdUtru/oZIMBQ+fOBIwZQ9DUqQRfcgmGlBS6hHQhN1Li2cs16K+//JCPUBIjm3qnrJbmNVkCrEHcevc/kO1NwUNbhztbY0IYe/XZ3P3wP3n4gYfQqb7fw69/+hbFoxzmaJ+0QfcgefXYjLvJW/NJs+0ORxVLlvyzMQABGD1qE+ecs5qzJn/LsKEzjzoAATCbzZx77rnc/Le/E5neE29dNfO//Yq33nqLrKysoz6vIHRkIggRjpuTXidD8cKH5zcth6Ue9pDeSY80fl9Vvwk8R1Z+XVEUcj6ZSea0c5GXFPHO/OcYTSZvY2aqXUY2h+EpryTsynTinx3V+HWg/PuX4MioauUKpxaj1sgtvXw5M0+teopye3mr+5bVlDR+X1NZ2up+geam4mROh/2o26bRaxtrm7hUD7L2yP7MWaISiJV8j58ya56ndNd8ABTFy4oVj7No8Whc7m8b9w+w3I7BYG7xXMciLi6Ov/zlL0y98hoMYRFUZ2bw0Ttv8eWXX1JdXX3crycI7UkUKxOOG1VVUFX1JAYjEsQNhIK1vsVv/oJ6/ptIYa3nhUSkXk9y3q9ke9dQZWwgf919WIL7oHpshHX/Gy5bHjU53xPe7U40et/z+LrsdeQ88Dc066uRAW+CngFPTOesoReRd89voA9AF1GFPqF5LkzsY8MofGwl7JvFt3zWVmIfG45sOHW71//I+YOPtn/EtgpfHRWn10m9q55wU/MqoIqi8Nv/XiV433KPbkMat2Xv2sk3X34BikJokJWYmFhqCn1DW+d+/inn3XDzUbfRgW8YjktqW8WxtDH3UDV3BTbzDjbn3Y4p61oc+grgZ3Q6cDqtBAZeTs8elxMScuKmLJBlmf79+5Oens7ChQtZt2wJu1cuI2PHdsZOnMTQoUPFIxrhtCCCEOG4iUnrys5li4nr2h1rROSJv6Asw42/wYrXYfHzbNmcye9z/w5AqN5GWEganQaeTc/r/OtGpI79AufcMRTp8tnjWgilCwGQlrzTOOtuwPwPSAiaRsO8EjyfrUEDqBrQXTWELv94E83+SfL27z+2lURErYzGtIW63+eBrEXfaTyFj4JmegrLa1fRM7wnXUK6nBLVVstsZXy0/SPe39Y0EiVQH8iNPW4k0hxJub2ccFM4W8q28N8t/yXIbSJw9laC9z8N6RXTeNymFcv5bu7v+5Yk6qrroLopJ6S2tuaY2to7vAuby3cD4LW70ZiaJiW0VdezafFaivILySsvxImbaEsEsZHRdOqaRoj5USpKZmKK2olD/0HjcS7nBCaMf+WE9H60xmw2M3XqVAYMGMAvv/xC4c5tLPjuazZu3Mi0adPEKBrhlCcSU4XjLmPVcuLSu2MOCj55F63K5tP7/kpdQxhTE27z27Rdt5WJD9+MrG+abbd441Nsq/xf47LGq+LVNAUChvUSYf/V+Z1HMhoInDARTUQEuohw9ElJVH5XieqoIWBELLqESEw909DHNOU95Nz8T2zLfvY7T11cMn8k5vDjEJkGk4SExNuT3mZYzLAOF4yU2crYU72HvLo8XljzAg6vr5roxZ0vZmzCWB5b8VjjoxgJCRWV+BITKUVmUgubim8504K459H/otfoeeu5pylzeprmdwHMkorDoyChEhMSxOU3/YXA4Nbn9DmcvWt38NHPvokLA1QjpsgdJESWkxh0Fd8vzziCM6gkJG4hLm4nIKPXT2bM6OfRtKGi6/GmqiobN25k3pw5uMqKUfQG+gwbwcSJE7FYmlemFYSTTYyOEToEVVXZtugPuo8ej3wSu4wrcvey8slP6BM6zm/9L9UOBvYtZcDtV/u1MXfxtZTXrSYycBgxQ16iOutrNBoTOkMoWc9OR7tIf/AlDkvSB2AeORVZ46HBoEX5+YtW9/1muMQXY/zfn/NTz+eJEU90iGCkxlnDRT9eRKmtKY+jV3gvbux5IxMTJzJ722z+ve7fTQeo0H93ML33NiVnuk0SoZMHccuVDyNJEp+89goZFdUA6L1u0tNSmXLpFZjMx7934ae3v2Jd8TaCgkro3ed3v23l5Qk4i86lV9pwzBYz2ZnZrMjd0Li9izWRHj160WviAGRNx0qds9ls/PHHH2xYsRyN044uLJJJU6bQt29fMReN0K5EECJ0GKXZmZRm7cUYaMUSFExM5yOvm3AsvB4vRQ8tb1zeYfey2+l7HhBlzmVa4myMV78LEa0P21TdDnb26te43HXDWuqXr6T8tdfwlJahOp0oTid4mvIN5MAIlLqyNrU1Y0JXHH+/iidWPuG33qq3suzKZW061/GWnJxM6rmplA70BSCxlli2PbiNm664iccfe5wNpRv4cNuH/JHbNF/OpX8mYqlvCp4m/uUOuo0ah0dRMJnMvPXc05Q6mkrjh+k1/N8DD5/Q11G8p4B1Wz7CGPRes22qCl7vYCLCzyI9/ULM5iA8bg8araZDBIGHk5uby88//0xF1h4AYrp2Z+q0acREn6Q6PYJwEBGECB1SRUEejvp64roe/fwgbfHG7QswSOBs4Td7jPVteprnwj07wRrTfAdAcTjY1bcpCIm+YjAhMz9ovp+ioFRUIIeFIcsy7rIqSp59g/IFP2Kw+/Ib6gICCKyv9zuusG88sRvzyTyrB1P/8zUANreNIZ82JW3e2fdObu9zO+1Fo9Vg6W2hblMdBr2BZ595lhkzZtC5T2d2bNiBFy/hU8KJujAKgA+nfEj94q2s/rqp5ydt1AS2Fxbj1JvQej14DniUIasKowb0Z9x5F5zw16IoCnPm9sZgsON0DCDc2o2ymvkYTUWN+7jdBiZN3IRGozvEmToer9fLqlWr+P23X9E21OE1mKg75yJmDOlPmF6k/Aknl6gTInRIYXEJGC0WcrZsPCnXu+nFkUSmh2AO0mMNb5pQT8ZDmnGpb2HeI60cDUX3/9NvWQ5t+ZOlLMtoIyIau8B1ESHEv/QQX49uqiNycADi1sg4Q33P7w/8tG3Wmbl34L2Ny29sfIM9VXsO9TJPmNVFq1FUhfpt9aCA0+HkrXffwq242bpxKx6XB9WlUrW0isTARB4Z9gj9Ivsx7IIrOO/eBwHfBLMbymtw6n3Dbg8MQPSql0cee/ykBCDg+zl5PMMA8HpzGTzyMaZOXUpo6MO4XL4RPTqdk1Wrnjsp7TkWHy1fzYxZHzDzq+94b+kqsqtq+HXjZtBo8VhDkFSVvevXMHbpJj4qLEfpWJ8xBaEZ0RMinDS1ZaUU7dlNYq8+mAJOTjlqVVFZ+1s2kgR91o1D591XpyMgGu7d1eIxNZ/NpvAx3w1J1il03dLyfq1ZuzeH1Y/MxGqvZsD2nQDkdo2g9/SZGFKT2Hj9xcQUOckZGMd/Lw0io6EOrVKL64BKn/tN7z+dm3sd/VDVozH006GsunYVklZCdfn+PBjiDLhKXSCDuq+LKS4hjvzc/GbHu1xOnn76mcblILMJvU5L2b6KqLHR0Vx48cVERLRtgrijtXfvfLJzbgXAZrNy7rSm3A9FUZj3R/fGGZTHjtmBRtP2XKCTYc62naz86vND7lOT0oVN6X2x7dyKR6sjsmt3nuuaQK/AkzeiRzhzHc39W/TXCSeNNSKSwPAICnZsw9FQT1Kffuj0hy5l7nY40BoMR/2MXpIlBk1NgdyVsPaAXon6YnghDQJjQJIP+NJgrm0qYKa4ZVRHA5LxyEcfDExNYuAn7/ut6wa4vC5WDetDTK1vXXVNEbuqfYW8Dpxc9qzks5ib7Zua/pX1rzA+cTwpQSeuJsXBrPqW/3hISMiSjGffHC1aueU/Hwd/rLF7vNTYmoqPFRYX88YbbzQu33TTTSQmJh5jq/0pikJNTR7r1z8D0rzG9SaTfwE5WZaxWm/DZnsdgD/m/5OzJv/nuLbleFmwchVmwC1rkDt1xllchLm+BodWR+dJU7hpcD8kScKjqPwvNpxXt++hftNaLi0o4OJuXbivUwxWragtInQsoidEaBdej5v8HdtwO53ojUaCo2PR6vXUlpVSW16KTm/A7XJitATitNWj0epI6t0PjfYo4uayXfD2SJrNI98KVYXs38NxVPk+EUddPYrQh99t+3UP8s+7e3PTb02JmXP7S3w8TsapbwqwknQST1mmU1KRxyeZX+LWgt1q4Nu71520ZMk1xWsYkuDLT1E9vj8PyZ2TydmTg9lspqHB12OTlJREdnZ2i+f49NNP2b17d7P1EfseX5WUlPitnzlz5jEXuquvL2fzlneorV2IJBWi1TYFky5nX2Jip9Gn9w3NrqEoCr/N6Y/R6OupGTTwT6zWuKNux4nw/bZdbPzqMwBSzjqH64cNBiC/sgqtTkd0YECzY4qdbmbuKWDFtm0E1tdQn9qdR9KTuDAy+JRIvBVOPSIxVTgledxuqosK8Ho8BIaFYw4KZv+v5f4/lh6Xi4xVy+g6YnTb5qdZ8CT8+ULT8t3bICgetn0Hq9/zlW1XlQO+vKCqlPyeT+VO3x92jRESX3se46hzj+l1PntDd85f6f/P7eNxMr8MN2HVqMTkOJn5gbflg3t2pdvX3x/T9Q8lOTmZ6dOnM336dACiYqIoLW4amms2m7HZbJhMJux2X6/GoYKQ/dxuN+XlvjoiiqJgMuWiqirFxWa++up7v331ej0XX3wxXbu2bSSV01nPn0vuRVEWNT5WAV8w6XKFEhF+K4MG3XLIcxQV7WDrtvOQZQWXsy9nnfVVhxruet8nX2LK2I4jJp6nb7mpTW37s7KOB3bkoGRsxWEwk5LenWe6xNPFYjz8wYLQBuJxjHBK0up0hCcm+607+JOaVq8nbchwdq9cRtdho47sk9zWb/0DkJvmgjXON+dMYCzqsL/j8Qaj6zG02aEBce9SNePfqIqE1wF50/9J8ueJ6Dr3OZqXCMCQmx/Aue5pDO6mQGTQVdN5apTvBrn5gilANgAuDZTFWUh0pmDocQn2la8f9XWPxtVXXs3LL7/cuGyz2QAaA5AjpdPpiInxjUKqrFzGho3XNW4bNRoKC7tjsZSh1zuoro7mq6/qeOCBp1BVL3ZHLRoZ9PpgXC47RqOl2c+9vr6CVasHI8u+ArpOZzBGw9kkJEwgIWEQRuOR5R7FxHSjpvYlCgruRm/YyK7dv9ItfVqbXuuJ4vYqKLm+CeyGDhjY5uBodGgg84d15624cN7btpuy9as4r6CQ67qlMT05GnMHq4MinFlEECKcMnR6A4k9+5C9cR0p/QYe/oC0if7L/zsLkEDWgOJBAnQAX4EXmRpNOLURA4m55k0s599KSmI3av5zI1VrNXgaZLIvu5TE35diaGNJesXjYemrDxL8/o8Y3ODWQM6Fg5j04Jt0MzV1o2sO+OTQa8s2JFWi4EHfaJ6AyU+36ZrHaubMmbz55ps4nUc2wd/h2O15bNp8a7P1sbHbG783meqIiclg3h/fIkmg0fjP++J2G1DVJEymARj0YdTUbkdVl6HX78tDUS/nrMmPH3VV085p51BQcDcA1dWrgQ4ShCgKFqcv+Osc1/Kw8sMxyDLTk6O5MCqEBzPiqd+2lZ+X/Ml3Rd15Mj2Js8KPfvZfQTgWIgQWTgnJycm88sormK1BWCOiKN6bQd++fZk5c2brBxmt8K8sGHwr6Pd/IlZB8aDK/iMgNCiEektJLv6V+v8Mp2zLAnJXvUNkchnJk3yPEzx2DZmjxrD2llsp+P13VG8rj04OULBxOYunDifi3R8xuiEnxYzuo1c598kPMZr8n+M7xgwAYGe6BVmWUWxuv+2e8tZnqj3erFYrnTt3Jj09He1BeThGkwmNRkNycvIhz+FwFpOT8w579r7I8hVjURSH3/aQkGEkxN9At/RnsVgG4fX6rqPVepoFIOAbRqvX78br/Qyb/XV0ugXo9U48HgNhoQ8xceLTRx2AeL1e5syZ3Lis1ycc1XlOhBpXUy5Tg6dtE/IdLMlk4KNeKTw9cQzuTukYdm/jXwuWct3mTHLsxyfgFIS2EDkhQoe0v9v95Zdf5pVXXiEnJ6dxW1BQEEa9jpKycoKCgqiurqZv375ccMEFhw5KXA3YqkrZumEVXmMo/f+8AoPSFEjslVKIVwsw4J/Amm9Mp4ohKJ8tQu9sCgycFjPewYOJueRSYsaNRTqgm9zrcLD06bsI/fpPtArYDPDD5CCCL72ULuFdiTRHYtVZkSQJSZLQSBr2fPwuia//SFWwltJR3ZAVFY3TQ5ddwXgK1oLiodvOHcf2xraiU6dO/N///R93331347oePXpw6aWXMnPmTBZU1KBRVcbs+gBG3HXY89XUbGTd+stR1aabpkYTQFrqP4mKOg+drvm/bY/HTVHRFuz2SgwGPaGh6bjdHuz2MgIDwygp2UFO7g+4XQV4vZVIUgRWa0/69buTgIBjG+7rctlYtLhfY/AzbOgqzObmswK3h3/PnU/tiiU4TBae/uc/jluuSoPXyyvZJXy6bRdhZYWUxaVwe3oqtydGYOhA+TDCqUPkhAinhQM/ef/jH/9AURS/7TU1NeyfY7X+oGJgh1JZ7+TVt5oqn2ZwPtfxbeNyFBWUjH2R+EV3IaNSroujPnIgURc+SXx4IvY769jz3XfU/Pobgdu3YWiwwcJF1CxcRElAAMqwocRddhm1rnLKHnucyFLfJ//VXSRmTZapCmyAHbNbbd+YLIU7gZBqDyE/bWlcf+Bn33dWvcrErlNJDU494td9JCIiIigqaqogWltbS1ZWVuPy+LB93fXhhw9ACgq/YOfOBxqXAwLSSUy4mcjIqWg0rQ/J1mp1JCT0b2FLLABWazydO0867PWPhl5vxmHvgiVgO263oUMEIIqi8MOW7ZStWYkBiOzR+7gmy1o0Gh5MjeXS6FBm7MrDtmMrny3O58suPXimayKjQ09OLR/hzCaCEKFDOzgAOZjX6z1078cBtm3b5recSRIfRD7CSNagVxowjb2bxJ7jqUvqhdteS3j3sRx4KzIFBtLruuvguuuw1day5+uvqf1tDtYdOzDW18O8P6ia55tLJRSossCss2RWd5XRyToCtUbcXjcurwuF5q9rT6xEnREC9z21yBmcgBoRysaKzQTZYG2axMZtH/LjiuX01vfnwpSLwSOxvytTAnQGLenDozEFtK3g1vjx45k9ezbnTJ0GAQH858kn0BzF5INer6MxANFqgxg44Gsslk4t7muz5VBdvRqdLgSrtQ8FBZ+iqm5MpmRcrnJ0+hBiYy6jtGwOxcXf4/HU7Xud0r4ZeH0zENO4DJIk73snJL/9fFTKy+djNMa32B5LgK/wmk7nZPXqXxg8eKrf9qK6ehZu3c6mnbtw2WzIJhM6QIvKyKFDObv78ZsfqbLBxtPv/peAmkoMQH1IOA+cPfGwxx2NLhYjX/dL47u4cJ7YnknA9o3cUZjPyPSuzEyLI9pwapWyF04tIggRzhi7dvlXPr3qqqtITk5Gr/e/YQemtPRp3J/ZaqX3TTfBTTfRUFVFxpdfUf/7XIJ37ESjKMzrK/HJOBmbUeKLqV/QPby73/FexYtH9SAjI0m+ImCqqqL83YNGAUmrpdu+IGDl8kf5NONbIuuSuG7tnegUX2/Cms05zdoF4HJ6GHJuyzf+1syYMYOsrCzOnjYNbUAAzz35pF9PyJFau+7Sxu89nhpKSn8hMeFGtFr//Jfi4h/Ztv3ugw9vRiObyNjzDC5X6WH3PVIOR/MqrwdbtuwnjIGd+GrjNuoKcjE5HBg8vsd0pn1fB1pYVEBE6C0MiD4+VWC3FBUTUFMJgDM5jekXnY/+BBYakySJi6JCmBDah+fjovh+2w62r1zG+MI07k7vxI1x4WhlUVtEOP5EECKcFtxu92H3yc9vfvPR6Y79U54lJIS+t90Kt93KkhU/MOeHb/mx2/rG7UVlRc2CEI2sQcNBNxXJt/5gjw1/jPsG3cesj75HVgzYtXW4Y2roFd2TAJMFSYLqEhv5O30l6YMj216i22q18vnnn5PR4CDN7KtQe+fNN7X5PIMGfsPCRU0TFWZlvUJ52Ty6d3+RgICmmYvt9uYBlCybUBT/IcDbtt8D+/p6UlP/hckYD6ioqPuHxPh9f6htKiqq6kWnDcJkSkRVVWrrNpOT8xYul28G5LzcHhQXd8bhCOTbL75AAwQf0B670YwuIRmLyYher0fVaCjbvhVTXQ2zv/qa9L/eiuU4BAtRwU1XffLaK4+qV+poBOm0PNUlnstjQrlvZx7OnVv4b3Een3ftwXNdExkYdOSVgwXhSIggROhw2lrN0el0HtGn9ttuu4133nmncfnTTz8lKCiIxMREkpOT6du37zH/sR817HxGDDmXuN/e5uPCj6nT13H3qruZvHMyj5/zOGbD0c3hYdaZUXxTt6DtYufev1/nt33XyqLGIGTr4nxCos1EJrU9sbvzMRawkmU948ftprZuKzXVa8nMepW6+m2sWj0FnS4EWfb14jidxY3HDB70ExZLF+R9ZeBdrnKWLN0/o3BT3nxszCXo9WHH1L4Dbd7yV8rKfvdbV18fgcPhnwuhJqUydewYOkWGE25p/vPb3acXH/73PcIqSnjqp994+sKjH9pbbbPz9s+/4TwgL6qorp744JM7hLZ3oJlfBnbhk7gwXtieiWHLeq4tKOCcbl14MDWWUJ24dQjHh0iBFjocg+HQ88kc7IsvvjiihL2YmBjuvPNOoqKiiI/35QXU1NSwZcsWfvrpJ1566aXD5qAcCVmWuWPqHXx+/uf0kHqgSipza+dy9idn8/vW3w9/glZ497VN00Jxqc6Dohg4NRmtTqY4s5Zvnl/HrlXFzfY7GSRJQ5C1D4mJNzNo4HeEh/tyGdzuKpzOYr8AJCioPwEB6Y0BCIBeH87oUesJCRneuE6rDUarPX6JkuUVixoDEKMhlr593mf8uD3cdPMrfvuFjO7EYzdey+CUxBYDEIAusdH0GjfB185N6/hs89GNYLJ7vDz6yec4tm9G3dzUk3bwEOmTRZYkro0NZ8Ho/vQfPgqN18Pq5UsZu3QTnxZWiBl6heNCDNEVOpxhw4axcuXKI97//PPPZ/v27YwbN86vp+NwsrOzKSsr448//mgsyjVq1CgmTJjQ5ja3RlVV/rf0f7y9520csgNUGGEewTPnPENIQEibzvXC6x9g3poAfSq586+XtLhPfZWTPz/fRdYmX02RQdNSGDQ1ud3nCnG5KnG5ylBUd+MMdxqNBbM55ZBtq2/IwOEowGJOw2RqOaG0rZYtH4vDkQeAxdKZIYN/82vD8sJs8pddT1BQMVqth8yAK7l54OOHDHRVVeWJ2R+h5GTSYDBx8ZVXMTS5bbVG3l+/lZwfv/ZbZxwwhPvPndKm85woq6vruW9nLo5dW3FrdUR37c5z6Yn0CDg4Q0Y4Ux3N/Vv0hAgdzldffXXEN01JkrEawnnp2f/w+uttK22enJzMoEGDmDFjRuO6lvJGjoUkSdw86mZ+vPBH+un6gQTL7Ms458tz+HrN14c/wQEUr68nRHuIuXMCQgxMua0X/c/yzUq75ucs5s/egdd97D08x0KvDyUgoCvWwJ5Yrb2wWnthsXQ67M85wNKZ8LCxxyUAcTiLmb8gtTEA0esj6Nf3w2ZtGB6bzPDJs9BqfYOjO9V/Ron90IXiJEni7ssvxW6xYnHa+fnD2fz794WNvVdHYkRiLA6973FYvTWE2HGT+Mc5kw9z1MkzODiA3wd34/pxY3GERVG7aS0Xz1/Jwxn51HkOX7hPEFoighChw4mPj+fb/32I4YBRK/K+G4VWq0WWZSxmC6+89BpdUtMJjwjj7KmTjkuSaVZWFqWlx28kxn4xwTF8eNWHPNr9UQK8AdRr6nls+2Nc++m1FFUXHf4EHPA45jCPniRZYtiFaYy9uiuSLLFrVTE/vroRR8Phk3dPZ3v3PO+3PHLEcgyGlkvwxwel+S1/uOE5PMqhb7SBZhN33HwjDZExaBUvtcsXc++777OjrOKI2tclPJTJV16NS6MloLaKwoXz+NcL/6ag9shr4ZxoOlni9sRI5o0bSPrQERicNhYuWcLoZVv4vqSKDtaxLpwCxOMYocNy2hpY9PrnWOUQOl1loKJ6ERpNOHGR03E7PbgcXrwehdi04KO+Rl5eHrNmzWq2vm/fviQmJtK//+GH67ZVVX0VD/z6AEttS0ECo2Lk1tRbuXnkzYfs8n/ypfcIyUjFNMjGTTcfWfJj7vYK5ry7FbfDi8Gi5YqHhhAQ0racm9PFqlXnUN/gG6Y9ftyew/bCrMpfTM7ufxGGrxck3zCa60e8f9jrKIrCG7/Pp3TVcjSqikOrJ2HMeO4c1XyixJZszCvk+1nvNi5Xmyw8d8/dGDpgMuiiyloe2JEDGduwGy2kpXfjmS7xpJnFDL1noqO5f4sgROiwasvLyNm0kR6jxiPrfY8gVFXZV5Dq+Jg1axZ5eXmtbu+cmsrV11573K53oN+3/s6Tq5+kSuMb1ZIoJTLrvFlEB0e3uP9jL7xD+N7OBA51ct0NR54nUJpTy3cvrcfj8vWkFIdq2BatwSvvG9Sq7hvAqu7/HkLMOp67uDcJoUc3mqclycnJTJ8+nVdeeYXp06czffr0xnL733///eHL7h+jJUuH4XKVEhjYk8GDfjiiYxRFYe6iHuhxYcfEtPFbj/h667Jz+fK77zDV+H6+auduPHzlpWgPk1vy8H/fR1uQ27iuwWLlybvvwnAC64QcC4dX4c28UmZt201YcR6l0Ync2C2Nu5KiMIoZes8oIidEOK0EhoWj0WsbAxDguAYg4OvxONiBo3My9u7l03ffOi6jZg42uedk5lw9h7OtZwOQq+by/YbvW90/fG8aVk0x2l2V1OQeee5KZJKVS+5rmnU4utJLSW4dm/Jr2Jxfw5aCGrYW1LKtsJbtRbXsKKpl+d4KFu46/o+lwJd3s3jxYr91u3b9P3tnHR7HdfXhd2ZRK2ZZDBZYYGZmxxQ72DCnaZM01DbYJE7DSb82SYMNMziOQ3ZiJ2ZG2ZYsi5mZtTjz/bHyyopkW5IlU+Z9Hlk7d+7ce3dXnnvm3nN+J4ONGzcOSH9H8fOzG25NTalYrS09ukYURcpEu76JE21sKFjf4/5GhYfy9F/uQDd0BDIgZB3h4U+/POHf0rcpaQ4DpMnDG++JU3n8rjvOWgMEQK8SuS88gFUzxxEyegKGxjq+27yZmbsOs7m26UwPT+Es5+xb31NQaEcQBPQuLrQ1N+HkMjB5LBITE/nhhx8cx0FBQUwbP5bVq1ZRb7QrZGaWVvCfZ57myuuvJzAktF/7N+gMvHjRi+x8fyf1Yj1mW+fkeUVFOzA3lxIRcyFjXb5gjMtX9hPvgUkyUO08BRkVEipk1LRYTWRaA0kXRh4rsQGAK/Y08PlqG4vn+DAmNBRBOCqDbq8jAHd+lkyz6dSytZ6N+PjMpLjYnjtIper5Cs+oIY9RcPhGnGlByrmVj4rGYUZDTNjNTA2ZesJr1Wo1D128hPf9/Mn/9WcMORk8t/IHHr5kSbf1123Y4EgV8H/3/KXHYzwbCHfS8cnQCFYN8uKxI/kY0lO5s6SIqQkJLBschI9WmW4UuqL8VSic1UQMG0X2np1Ej5t48sp9QKPRED9kCGlH7NoOJSUlfLZipeO8YGpD1mhpssL/3n6H0TFRLLjq2n5NJAYQJLgzobkEfea77KnaSGHJTsYYTYQek7p9TGflc3RiK0Fta7q0FSurqKichVXufl9+jcFC44ZK3qSSyLYCDO4ejm0YGWi22IWxUn79mR921DFi/mKChyT211s9I0iSmcrKnxzHlZWr8fdfeIIrOhjmPwqV+BkVKXbDIci8C4C2rB1UeO/C33DyUOsbJ4/ndZOJyi0bMKcks2LwYC4ZltCpjsViwafO7n/S5Na78O2zBUEQWOTnwTSvJJ4d5MM3aRkc2r6F2SXR3J84mCsDvM54uLjC2YVihCic1QiiiNVswmI0otH3v7ObKIosmDuH3F9XYQz6Tb4VSWKx2ULTzp0cjoun0t+fvdl5FD74IJfedBN+cXH9No4r6wpY0lAD1EBlPmOOU69Ircbplg1Y96Ug1xWCbEOQrQiyDUE245f/JirBhm5yBQa9j+OGLwDFNQLfZYg0ih1LJLlOYWDuvq+W3MNktuZhNhkJHpKI1Gqh8ddC1H5OuIwP7PV7FEV7fhxBEBxRFEfl9gfaNW3b9qkOaXaVygUXl94lm0v0TcQw/HsOlW3CteL/AFBjo66trEdGCMDts6bxj9JS1DkZbN20iZmDI/A8RgBNrVZjValQ22zEDB3aq/GdbbiqVTwTE8ylAZ78Pb0QbWYaL5cX8VX8UF6ICyXmFJV5Fc4fFCNE4azHYjZhMZsGxAgBcPH0QtNYi7q5AdfAYGxJs3gtS0Ni8REW7fseP6sJ37JKcgZHcXDYMCoNBt765BMmBwYy7bbbEPshr0eCtSMcudrgSbK7D/UGD+bM+T+MjaVUH/oUm2cYCdMeRa3Ww8Kuk1T6/nfwy7e/XrJgGp6eXZPYPQDUt7Vw4yN/w6PRFZARZMDJhYTRo8nevQ2LyYSTrY3QNrvD7tHn1tqvszCm2cNNDcP9EPW9u334+vpSVlaGSqWisbGRxsZG8vLyMJlMmM3HsYT6iaMGCMDECev7JP8e6ZVApFcC77YUEd5s3xYrObiY7OBnWBTzhx61ce2sGXySm4l7bSXP/vdVwkaOJiE4kGGhwdgQUNvsYcBltXW9Ht/ZyEg3Z9aMjuPtAG9eSs9Dd2APFxcXc23SEMVxVQFQjBCFc4AhU2ZQnJZKxPBRA9bH0vsf5dsXnkRtNfNsut0xddegBJ4cdyPPbXsTAYiYMJbR8xfx3edfUO5sYFNlJUWPPcZ1Tz/taOfIr+uozs5i5MUX4+zXWYOisrKSbdu2kZCQQExMDHXp22hd/yIBlZsZjH3yKUi6l7BLljHn2AsDhhEQc/JoGOMau+haiVpFoHvYcet5ODmTE1OJrN/KzK1DCW1uoMZ9BGM0lVBzqEt9g7v9SV9qO8ZPpA8ZVWfOnMkHH3yAwWBgx44dLFiwAEEQWLFiRb+vhBQXf0prWx4atQdqTee8Kzab8ZTavnnss/yY6odTpV0cr6HkDeihERIbGMCYpZew9efVGNpaqdq+mY3AesCi0qADjBotc4af2yshx6IWBf4c6sciPw8eyvSlPiOdHzZu4IeYRJ6Nj2CK18D4eymcGyhGiMJZjyCI1JYUEz5s5IDtJ3sH2R1O68tKGemcwX5/+3K9SWX/L2IVRRIffhRBEPjj0KH8+sorbG9sJFejYdeXX3Lg4EHKNRrk9vGte/11vBsbCfD1ZdGf/oSTpyefffYZ9fX1HDx4kBFiOkukn/jtQr73iJ75KXSHpn0e35N0IUtPoKoK4KxT0yyAKSgA0htQm5vJ3rPDcX7atTcj2WyIKhVxE+3Olx4LI6hdnol+sEeniKWe8tBDD5GXl8eKFSuwWq3s3rULmyRRW1vbLz42kmSlrm4buXkv09h48Lj1qqt/JSTk+lPqS+c6GtqDh7T0zon3wmGJTI0ZzFu/bqCsqAhVYwOuxhZ0NgtGrY4LLr+C6YMjTml8ZyMhei0fJ0WwKsCLf2QU4JyVxl3FBUwaOlRxXP0do3zrCmc9ao2GIVOmk717B34Rkbj7da+jcSoUpaXYXwgCD8/yIvOtt2jQOjOt5AAAklbjMIBEtZph8+axfflyAH46cgSOUXc9So2bGzUmE4dffpnLY+MYM2YMv/zyCwBLpA4nyayE+3By88Rn/FW4uPt0aaenHDWAgjyjTlivPD+NGcktuBvnUS7aU7O7V2Vx7AaAxHAQZRAFrBZ7bhBtsCsB9/Z9NcrNzY0vvviCn3/+mVdeeYVt27bhpNNx71//yueff8706dP73HZt7TZSD9+DxVLbqTxw0OUYjSUYTaWYTFXo9YF4e0/rcz9HaWrJ4ej6yvTx3/f6eg8nPQ+054SRZZnt2XnkVVczLykBXxfnUx7f2cpRx9WpXq485+/N1+nZHNq+hTlFUfx9aIziuPo7RBErUzinSF7zI5EjxuDu59+v7TbX1rDy+X9SmZ+DIIr4hIRRVZBHYF0Twwsrwc0Vy5v/w5SexqgrrkA2m3nyuec6teHT3IybwcDFd9xBcXIyO5KTKWhPjAcQUFZG3OWXs3HLFmayjansBqAo6S5CLv4nnOLNN+vFMKJb6kmZ/TBJkx9wlEuSRO6+DWSv+hzNtmQCi1pJi7uW8oDxSLZazE2fg9wxTkHlg87tOsdxWKI3i+4cdkpjO5YDBw7g4tIR6mMymUhISDjBFcfHam0hL+9liks+RpLMqNUeBARciJ/vfDw8Rve7rsxRKlpKSd01BQAp8i3mhM8ekH7Od/Y3tvD3jCIaMo+gtloISBzGC3FhiuPqOYqimKrwu6AiN5vmOvsTr19EJK5efV89OBZZlvn3FYsdx6JKxQW33onl748w98UOGe1nPnuLq/56N9qoKA58+hmis4Fhl17a7RPcnm+/Zc2ePVh/k9fGmRZu4Qs8aQSgwH8eYX/+6pTGn/mvcGKa6zg46wEGD7uN9F0/U/PfV3GuasarrmPLQAJSEv9EjU8SoraFJn8vzM3luJly8XICz8DhOHuE0FRrovBwDf4RncXOziayc16koOBNALy9p5GU+AYq1emRpV+5YQJusn1PZvj47Xgb+tcw/r1glWTeLq7iPxkFBBRmUu/lz9XDEhTH1XOQvszfynaMwjmHf+Rg/LEbDZV5OVTm5eDhPwjv4FMTEhMEgSFTZnBkywYAlv79USJGjKbugfpO9VbHj2TC0ovwuuEGRjxwPwDmtjZ0Bnu4ZUtNDaXJyZQuX473lq1cLEnsGzWKnOiOpGgtOLPG83quqPsvAGEVa7BZTKg0x59Aj8QNcbxWzVhI+H+eQ3tMhIp/cRt4gPcT/yO/+GMMwNEAULMKiod4o502iYixl9L2P7sq5/gEDSNum9RtfwWHayg8XIPNemYz8J4IF5eOMOnEhFdQqXRYLA1YrQ2o1e5ofuOU2p+4h/8DW97diMhs2nUhw0d8QaTH+efLMdB0dlz1pi4zkx8Vx9XfDYoRonDOIghCu0EymIrcbHL27SJs6EjUp5BNd+5td+HuF0DcxCkOo8bzsst486uVfGoVCVQJXF6RD0DtBx/QlJ9PXUoKLjU1tHh4IKtEDLV1iLLMsbExw1NSiMrJITcyEpObG9pJE1mS83fH+YLQSwk7gQHyW2wbVvHzm1dx4T32BHuy2Yy6PTeMoJYRgXpXkfogd8wxIcx4+FUSXb3Z++av/PpODTaNJ6LNjF9091lkAVQq+8qOsdlC5u5yR7ksg6gSCBnihd751DMXnwq+Ph3bIJmZ/6SlNYfGxkPY13sABIYN/R8+PjP7ve+ZEQvZqnHFlHkj7nI1O9JfJHL86/3ez++F3zquGrLT+EtxIVOGJfF4lOK4er6ibMconDfYrBYytm8hKC4BrasbKSkp2Gw2dDodoigiCAKiKKJWqwkLC6O6uprm5mZ8fX1xc3NDq9UiCAImk4lNmzah0WiYNm0aoigiSRL5+fkUFhai1Wpx3rAR7ccfH3csZp0Oq4cHtsQERv3736h0XQ2Mln+G4CzZt2Okf1QjqtQn9AvJuXAJ5sxMAEwaV7ZNeo473uyYXFv+EoizdwumBjU1925hUMgQxxZRRXIO697cR51g37rybCti+pWDCbyg+1UQgPLcBla8sO+456NG+HLBbUnHPX86sNmMbN4yCkn6bditwLG69dOnpQ3INo1Vkti0MRqAGtfFXD7mpX7v4/dIo9XGs7llrEjPJqCymKbQKO5PiuEKxXH1rGbAt2PeeOMN3njjDfLz8wFISEjgscceY/78Dg2DHTt28Mgjj7Br1y5UKhXDhw9nzZo1ODk59aYrBYVeo1JrGDJlBulbN7Jh734qG3qXPEsQBHQ6HUZjx4SWk5PD5MmTWb16NY2NjZ3qJw0ZQvyRI7S4uuL11FOYGurRAB5JSfgMGcLJqB3+Z5z3Pw+A+JTdOChb+DGDxlzYbX3DXY/QfO+dSIKKrOjLmHpFTKfzklsssB/ByZnA0HgAqvLq2PPORvKrnJFFH1RWI8NC6hh7/x9QOZ3Y+c8vzJWEKYE0VLV1Ki9Ot8fRtDYNrMBYT1Cp9MTFPU1u7kuoVE4EB12Dj89M9PpBrFs/mKOGyMZN8YSG3Ex09MP92v+xGXGt5op+bfv3jJtaxbMxwVzm78nf0v1RZx3h5V+K+TJhGC/GhRKtOK6eN/RqJeSHH35ApVIRHR2NLMt8+OGHvPjiiyQnJ5OQkMCOHTu44IILeOihh1i8eDFqtZqDBw+yZMmSTplJT4SyEqLQH7z51DLKLXKnlYXg4GA0Gg2NjY3U1NT0uk2tVkt0dDSiKGI0GjGZTERoNIxduBBnL68+jTPtP0uJb9jQqazh6p9xHzy+21WRirxGDvxaSNL0IAZFeSAcIxrWsvy/OB/+BxaLAc3TZWz/eSPJ33b4czhL9Vx4RxJew6L7NNajZO+rZM3b9pT2gdEejJgbSlCMJxrd2ZXpNT39H5SUft6pbNbMnH7v5/WttxNrXoOEwOwZWcqTej9jaXdcfTk9H//CTOp8BnHtsHjuClUcV882zkh0jJeXFy+++CI333wz48ePZ86cOTz55JN9bk8xQhT6A4vRyEf/fp4ik+SYzMeMGcPChQuRZZm6ujr27duHLMsOfQqTyeQwLrKysjqlm/f09OT2229Hcwr+JsfD3NpE8YpHicx5v1N5UcgSgq5/B1HdVYOkO1p/+hjDrjuRJdhYNhrfbVVsmvpSRwVBYlBcPZMumox/aN+1VsrzGljxfOdtmkFR7lz894FTtO0rbW0lbN/ROdNtfxsiKTXZVB6cB0Bl4P9xZdzSfm1fwU5hm4mHMktIyczArakOYpJ4LiGcyZ6K4+rZwmk1Qmw2G8uXL+f6668nOTkZHx8f/P39eeWVV/j888/JyckhLi6Op59+msmTJw/om1BQ6I7Wpka2ffMl2zNykTVaEAQefPBB9D3MQZOVlcW+fftobW1l7NixJCYObCbZqsObkH+4Bz9jbqfy6qQ/4nPJiye9XmprQXy+I7Fc9k9+rBi6mMyxI5hRaaGhKNxxLiixgXk3zsbJufc3cFmWqSxoInltIVWFjTRWG3Hx1HH9s8f3L+krsiyxP/ka6ut34es7j8BBl+HsHIOTU1Cvxrt+Q0dkUlzsUwQFXdmv4/xm/RjcsYeNJ4zdToCLEq47EMiyzI9VDTyaXoBzThrNBjemDk1UHFfPEk6LEZKSksKECRMwGo24uLjw2WefsWDBAnbu3MmECRPw8vLiX//6F8OHD+ejjz7i9ddfJzU1lejo7peATSYTpmMEnRobGwkJCVGMEIV+QZJs7N2yhbXfrkAWVUSNGstVV111pod1Qko/v5vAjA86lVWrA2nxGQZ+Q9AHD8V31IWdEueZsr7H9uNfMDTU2+sb3bku+mn2RybxfpSZC0LGkHVoHXtWZ1FfEA2I6NyqmffHeEIG9924qshr5Ovn9+Lqpee6Zyb2uZ3jIcsS6zd0vXfo9SEMHvwAfr4XnHT747dGSFLS6/j5zuvXcX6fuRzn4gcBKBOjuWb6z/3avkJnjjqufnMkC/+qEhrDonkgcbDiuHqGOS1GiNlsprCwkIaGBr7++mveeecdNm3aRH19PZMmTeKhhx7imWeecdQfOnQoCxcu5Nlnn+22vWXLlvHEE090KVeMEIX+QpIkUlNTWfnF5wiSjRtuv5PQ0FPTFDkdtBSn0fLZ9fi1ZnY5d4QoYu5fj2BtouW7a3HNSQbAohaoHnEBBSMe4rPSAoL1Wu5PvMBxnSzbSNuzmm1fWLG0uqN2auCyh8fg5RvcpzGW5TTwzYv7MHjpmH7fcEQB0kobKW80UtZgJKW4ATcnNW56DffOiSHQo3cO6jZbGxs3Hd9IcnVNIDzsdnx953arjlpbt4Pk5Gscx54e4xk58tNejaGnfL55EX7WIwCMmrgfD/3AaZQo2Nnf0MLf0gtpzkpDkGUCE4byQqziuHqmOCM+IbNnzyYqKooHH3yQyMhIPv74Y665puM//R/+8AfUajWfftr9f3xlJUThdLFs2TLUjXVY3Tx5/PHHz5knptpDa2jZ/Co21KhqMgmRix3nrCoBtc3+X7g6NBSnRe/h7DfmpG021VXz1fMbMNZ74xtTwOX33dinsaUeqGTTm6nUihLvuplOWn/2ED/+Ni+WuICe/9+2WltISfkztXXbUKvdGTN6JeXl31JY9A42WysAbm7DCA+/Ax/vGQiCiCzbWL+hc/SQu/tIhia9gVbbPwq7v+Xr1DfxrLRvm5Vqx3Ht5M8GpB+Fzhx1XH3lSB5+RVnU+gVxXVKc4rh6BuiLEXLK35AkSZhMJsLDwwkMDCQjI6PT+czMTMLCjp9WXKfT4ebm1ulHQWEgmD59OsgSgtnoCDM/F/AaOo+QO38g/M6VhDx+mIzQDiNfbZNpcFWza1gILldt7ZEBAuDq6cOMq2MBiarMMPauW9unsZXU2Y0AGXDRqXH+TXbdi0Z09t349UglF7y0hdqWnof3qtXOJCa+ipM+FKu1gazsZwgJuZ6JEzYRHn4HKpUzjY0HOXToj6zfEM3uPUu7GCBJia8xauRXA2aAAFyScBs1GnsOnEDzrgHrR6EzGlHg9lA/1k4dTsiYiaiMrXy/YQOzdx5ma13vwvQVTj+9MkIeeughNm/eTH5+PikpKTz00ENs3LiRq6++GkEQ+Pvf/84rr7zC119/TXZ2No8++ijp6encfPPNAzV+BYUeM2XKFKxuXmjqa/jwgw/Ys2cPNpvtTA+r18Te9BoFURNo04ukRbuwd7g7ze5tbNi4+OQXH0Nk0lCiJti1LfZ8a6Wpru4kV3SDZF+F0ahFUp+Yx+F/XsChZXP59+XD2PnQLP7zh+GkLJvLcxcnoVN33G6e+OEwktTzRViNxo24uKcAgerqX9m950JM5kqiIu9j/LifCQ6+3lG3qSml07VBQdfg53dy35FTRRAEXCy5J6+oMCCEOun4ZGgET02fRMvgeMhI5fZft3DXkQJqzNaTN6BwRujVdszNN9/MunXrKCsrw93dnaFDh/LAAw8wZ84cR53nnnuO1157jdraWoYNG8YLL7ygRMconDXs2rWLn3/4HrGtBVQqYkeP45JLLkGlOrs0LnqC0VjWLsoV5SibMT0TUez5e7GY2/jg4VWYm70Ye5mRMbMW9GoMP6/LI2d5Ho1aeOiVE0ujt5qtxD+2xnH8+tUjWZA0qFf91dZuJz39EdqMhajVrowb9zN6nT3c2GJpoLEphezs53FyCsbTcyK+vnMc5weaitZ6Und2hClPnnoEXQ/DqxX6l0arjWdyy/guLQPf6jIaImN5aEgkf1AcVwcUJYuugkIPMBqN7Nu3j/U/rUZoa8YjIpoJEybg6+uLIAiOn6NS7z35Abq8/m0ZAJKE1NICRhOiizM4zgsdwmOCgNCuxCm0j8HR5tH6x7xe+8tCNNosACyWEGbNXAmIxzhqHn0ttkumdBwDfPH8p9TmBxI1oZKZV1xq7wvsSWKODqnjBTL2cgGBb37OoWpVCZWixCMvT+Po3USW7VEpYN+qUalVyDLsL6zjqrftWxX/WDiEW6ZE9vr7M5kqOXDgBppbMjAYIomMuAcvr4nAyY2v7iYgU2srVpOpQxyu/b3h+NzbP4HfngcQVAiCGlFUU2asIyd5BoIILSHPszDyItbu2YdYbcXZ0xu5YBNt7kYITsTxwSI42gewyGaOWOpwdvVGRAQBRASE9u9OEAQEBARBbP/2BERBRGz/GxLbzzta7BiyHVnueP2bj0JqlfGxBKIWNce8b8eFnT/HY18fe+rYi+Tf1KMnyB2f7W+ukX/b3gkaFICSylZ2bSuhERva1gKsajX4RvLwJfEEBCnaIgOBYoQoKPSCjIwMvl2xAkt1BTa9E7J24D3qPerqmLemb/4XPcUYL1F7h7Wnd31qM2ZTefAPp9RnpSjxYQ8cU4/l7/NiuWPG4JNX7Ibm5gwOHLgR0ylIpcsyZH4TTlt1P6eUEGQsKi0aq8VRNN53MWEudin90XNdjjuDulX9B13b/v4dTw/QWp24dt8TaKT+z69ztiBLLUjWElTaIMZdmMiIuWGo1Irjan8y4LljFBTOJ2JjY7nr3nvZsWMHqdu3IGn1oNUhy3K3P5IkHfcc0OV3d4iSdNxzfUESBOpd3ajy8KLG3ROf+lpi0vIRTCD30KbyjF6HsS6cxsJxfR5Hkbp370uvERkZ6tnn/lxcYhkz5juKiz+ktOxrzOaqXreR93NIZwNEkNsf+E9xuV4WOhkg0L561M41Rbkc9lO3lx9TR4ZCa7XjWKUL6TiB3N7G0THKjh/ZUXb0O5CP+x66/mXa67k169FIOiQkWpyPMy0cbfI3jcjH+bi6/19wnHH14iM/3v+u7tpQWWSskc74DXLGZpVxKx5EU/oRtq9YT+aeBGZeO4SASCWU+kyirIQoKGAXNcves5OYcf2n+nmsgeI4liRM2dkUXnSxozxy9y7kY4wTWZLg6LWybN/aoP310XqyzOH0LO6sMVLs01md88LNv/DsVYvRenthn5iOjkFCpr3tTpNYe5nNgKjSd8xvx1zZZf6RcTzNWyQJq6pjBhCPbge0n1/1yvOUZWYw4ZIrGL34IgA0KhG9pn/8cOyfc08cDzvf6vIPJrPyuY4UE7e/+zE6g3Onuo7v4JgPpOM7bTdMJSuybMNmM9NiMtJgMWC2WGhtbGbLU/cDEBc9mUn+g2guiUTrVIzvY1d0uzU08f0EmkSRt+L/xMQxd/TmY+gzLzz7Cc4FdqVdm2DlrjfmnpZ+zwSyLJO1p4LNX6TSWp+NoPZl+Kwkxi2JRKtXnslPFWUlREGhj4iiClFUIctyvzmudfEHAVCpEILtwmAy4HrtNSR//z3GpibChw4lfNLJjSBJknjpuzX8x8UHi4/9Kc6nqQFRlql08+D7qXN4xtsP1z4m1etvxsycxZqMFAp2bmTGZZf3e/v2z7n3OX2Ch3TOdWMxWtA5qbCazWjbs37Lskxhxi/kZX1BaORSIhO6z3B8FBfgWJNwy9Fyd09cl0ym+fV8zG3BmPbuRz+mc/9vr/4jTe2+QBZb77a2TgVVWcdkoZLV7DucyqiEgU1RcKYQBIGYsQGExHuxbXkgR7YdInnNBnIOlDH9qgTCkwYuhFuhexQjREGhnaC4eNI2rydh2qwBaV+22Gg7UktzZjWrLr2WZrUZLBbIzgZA+9Ma5qc0kOejwVlnwypJtEjQgkyrDC2S/fchk5XtQeEATCzK4/VZ4wkIGk6r0UjkjnQAJHX/J9rrK/4R9uidtqbGMzYGWZLZ9HkGh7eUEprghbHZQmVBEyATNLECtd7G9s2XovEoQbIK2ExaBJUNtb59hcUZsgq2ndQI+S1uGh8aLdUUZx9GFXobBp9fqSoPpvSzncS6WVFpNBRaWsi21PJK1Q7HdcvS3mPdmL8gqgb+e4xa5EzJNx3HeQWl560RchQnFy2zb4wnZpw/Gz5Jo6H8CD+8XErshKFMviwag5sS1XS6UIwQBYV2nFzd8AkNJ3PXNnQGZzwHBeHq7dMvKyO2BhNlz+4GoFJooFnXVaxLUmnwzXfFNx9Gzzux974oSfy9voy7r+7IISOcqi/DAGGz2idyUX3mbjcbP00nbVsZAIWH7YnmZNlK1MIiXINb2mvZjSRRLSOqu65EaKxDe92v76AwGgurKa/PpT6nBNvYCH587RUAtjxmz9L87eQSLBoL6HFscVWrBNpaq3F27V0Ic19YOncWzIVn//EphnpX6vOtHNqT0dXfqd0XRZJp3xq0b1PJkowky8iy5NjKk4FBwX5ExPYtHcDpIjTem6sen8juHwJIXptC+rYtFKSWMuXyRGLHByjhvKcBxQhRUDgG/4go/COisBiNNFSWk5Ofi81ixtndk+D4vj8dis4dT7R+cveOcFahQzhtaZGZwy6NONlsGGwWDJINgyThJNtw02i4aFQSo2eN7NzAMTfM+et3oWr3YxCBG31duG3ShD6Pv7fUp22hdc0zlNbU8oJwPVLMIm6Vv4dXRkJrNdy6AbyjTt7QKbJjZQ771xQ4jt39nIga4Uf6wVyMNaZjDBBQt01Hli0EhS5AQIdG50pbSzmFlf9EpbWh10X0fgBt9u/AWe2BS7AfJi9DlypPfWLErwH2Rwm8dImAsV1q3OB8ejPxWsRK6nyyqKuE4lWHTr1BGW654U8ER5wenZa+otGpmHRpNNFj/Fn30RGq8g/zyztlZOxKYvrVcbj79nP0lEInFCNEQaEbNHo9PqHh+ISGA9BYVUnWru0MHjuhT09Hglpk0MPjKP/XXmSzjetaJ5GqKWW/Jq9TPTNWtKi5Qu3E9IvH9qoPrVqFS1szzU4ulLh33tt+r6qK23o96r5hrs7H46tFeAC1mnC+kh8C4DLD08ytfcS+XvPuXLg/Z8DGYGyx8O5ft3QqG70gnHEX2nVJtm7dgsESSmPhGNxC9wDg5TeDhFFXIYqdwzZLvl0J2gO0tKb1agy73/2CnCp7uO2sa/6EWqdBpfXg5uf/x4on/kF9ayVa0Qm/Bnv9kTkyl6+NoNnJit/VVzq0Yk4XZn0DajOIshoRNXTS6+iq+HGsdoz9dUcdk9wEgszh/VkYWy2oVCJqjZqgcF/EszSfi1+YG5c/PIaDvwaw6/tUCg7t4rPMYsYvHcawWcFn7bjPdRQjREGhB7j5+qFzdmbXyq8Yf3HfNDVUbloC/j6amk+OQAGMtEUSKflTJTRSITYgIqBpF9xyK2/rffsqFWtGxbAtr+PJf19VLV+6+GPrJsPsQGEpOczRHfVAOkJOJ6sPdUxT4/88YP3bbFIXA+SG5yfh7H6MBoZ7AXELn+xUp7Lpccp/XMucCz/qVG62laAFRPcj/LR8KpKqFr1HG5JZS5D3fcSOuB5VuzKqzWgh7au1HN6xnpJaex6tuOjJRM+3q0YLgoBHeCBXvvh/LH/oYaobixz91DjrEWUBt1YNlpw0mN1fn0jPsOqMqM16omdGceW0K0+pracefx4rbexIWceOzir6+DiF4uXpzRW3LO5i8J1pVCqRkfPCiBzhy8ZP0yk6nMaWL38hc08SM68dgm+oInLW3yhGiIJCD9EZnImdOIXitNQ+b82oXLXoh3hhLrD7H3jIznjIzkRLnff+l4WK+KbmcUeoHyPdnLtrqlui/P2I8vdzHIvbt/KlCdA3k3nwK2KG9X90ylGOlFfy1z0pGK0SbsNeBsAmqpFksAoqmk1adpdFMSv1V3x3NTItvhI3H7+TtNpzZFmmKK2WH/570FE2+8Z4Ysd13Q740z03sHvvR13KZW0yVqsZ9TFy6y6GUZj5GQCtd4mjXNSaKWt6jsJVrzFh8reYcs38+Mrz1B0joDZ6+CKm3P/HLv0YfDy54t//YsWDj7B6qIxKklHrXMFq3x7Snkaj0UF75LHYD31HhcaSVZjqCG2Wj9lqrG4rpLqtkIyUJIYM671i7unAw8/AkntGcGR7ANuWp1GRs58vny5i1AXDGLMwArX23EvzcLai6IQoKPSSmpIi6kpLiBo1ts9L5pbKVswFjah9nLA1mbE1mLE2mvi1oJqvvQU2+dl9SFQCLPXzZK6PG3O93XHq5ZLw53tWcm9zBHq5jWe5D88GG3Mu3IpK1dX739jSTFNNNXu/X0FLQz0anR5ZlhzaJGZB5O4x9iR50Y1VqCUJmXaJLBkyPXvuw3Dv28tQ26xc+/wr+IWf2kRUV97Chk/SKctu6FQ+87ohDJnYvWOnxdLG5i0dhqS1OZagQbfgHzQU38CuKq6FWb9Qkv8LVlsDnl5JVFVuoqU1G41LC2q9DXOjMw0F9s+0JtWP6NDZDL98CX6xJ/Z7sZjM7HjtI1L2/oLR1uGf4jQrkdv/+FyPP4P+4IF/PYBTsxPxs+K5fEr/G6ubf95NZUU1qXl2B22VrOPRJx7q9376m5YGE1u/yiJz12FkqQGPwHhmXhNPcNzZEQJ/NqHItisonCYsJiO5+/cQGDMEV+/+0xaQZJkai5W8VhOvFlaytqYjrFUnwAinFkbqa4h1D2KCXyyhBpcTtlfZXMnQPaUAuMn1vM7NCEB85KcMCh8PQFl2Bnt//JbMnVvhBLcDk1bHKzc9etL34NdQzZ2GFrycQat3QhAEXk0u52BkkqNOQkYyCzaswN0/gFteeeekbZ6I1/603vFaECAo1pNRF4SddJLY9OMyjMKPqJ3qsDYNYd6SH7FZTFSkrcXdbyjOg8JO2nf+kZ/ILrmTYxcPxGYvkib+iKeXD+aWNsqay3HTurJ67/ccrDyARqXlYGsqxZpyAIZIgxncOoiSkhScTCp863QEjB/OHX98vm8fSB954MUHcGpxImF2ApdNvmzA+nnp2beoN5UhyCKPP/HYgPXT3+QdqmbTZ2k0VqYjiO4kTB3KxEsGo3c+e8LhzzSKWJmCwmlCo9MTO8G+NVOem4Xe4Ix/5GC0Tl2jH3qDKAj4ajX4aNQ86f4LU6u/ZjPT2c8YqvFjZ6szO1udoRbIy+ZCQy7PDZuNl96NVksLBk3nrZsPc3cA9sk0nI4082m5V3P4wHDqSkuwWazICDh5uyG1+eDk4oazpxdDJk+zJ9BrX+2xAa8c0/ZfaUIrgCiK7YnTwEWj4ZKJU3DWdc5BcmT/2xyUJY7O1odjRzBj+2pufrizX8apIstQnF5H3ISTh7ZOW7SMrWs0mHgPteuRTtmIqYHgI7cRO/P+E7YRPmQ+gvgGhbnfY5YOstZcyZ7WVqpWzcbH5kkrbbSqjJ0vkoBj5q0jYjZHXLIhtqNsnuX0R5QcdTTtj+2YE3Hx5Yt57+P/IQsSKXszcTLo0Dlp0Wg16LQanJwN6A1n38QeMdSHoOiJ7PzWn4MbUkjduIm8g2VMvTKBwaP8lHDePqKshCgo9AMWk5Hy7EysFgs2qxW9szPBQ/oe0tvSksPOXR3y2TptIIWSFwdJIl+Io8jqQi7hjvPxqhLSbEHM0KTxRMJ4YjztWxxX7fyR9W3BTNJk8+XEi9m59m5M2tXd9umkjWXi5O7PHSW31cTcvRk02yTGuDnzlzA/Znm7oerBDbi0ro7alhbmZFUjA2/Gh7LU/9SXtLd/k03y2sIu5Xe8ObPb+pIkUbSjjIbiZmyGYmqdu/psHCXO70WCEi8+7vljufunC1lfmXfc80E2f+a4TUeDhr0Ve0l2SWekLYEh6mhqbHXskvdTp2oCYKz7CN5d2tVnZSB58IUH0bfqGTp3KBdP7Nl77gtFuWW8+9Fbx68gC8QEjuCq23onDHc6KctpYP1Hh6kpSkMQDUSOTGT6VbE4e5y/CQB7grISoqBwhtDo9IQkdIhZ1ZeXUZadwaDBsSe46vgYDJEEB19HcbF9IjKZy5gUdS2XBV6GRuOJLMusLtzJ3blWmnElzRYEwAZLPBsONBIpriZUY2ajKRyAsa4ialFk8gX/Zd+mQJqbsh35Y1Q6I2bVbmSx5Tij6SDSoOPthHBuPZzPnsYWrkvJI0yv5bmYYGZ4n/imE+jpSaCnJ1OrTWyqa8J2io8/OcmVbPgkHVNL17wxl9w/qkuZLMvs/SCNQ3srMTo6F9A4P03Uwkewtrnj3JLAuMVvsHn7CBAk0iv/jrjNgO+oaRTv+4rG5kOoVW54DBqOoFLh5jcUZ69QAHZU2Q0QH7UTLwx7idr6Kjw8vBkVM45mUxMeric3uKZ+MZU6Ux2DfEJ7/DlYJAtfpH/BrwW/UtFagdFqZNnEZUwPmd7jNo5loJ/og8L98XcNp7apCkm2Ics2ZCS786oACDJF5bknbedMMijKnSv+MZ59awax58eD5OzdTmlmBZMvTyRugiJy1hsUI0RBYQDwCBhE5c6cPhshra15qMRjRZJksnOep6Dwf0yZvAdBEFgYNgGTdQMfl+Xgp9UQqpP4ts5AkexHrhRI7jGinyqxwxF11LTOzoDNzZns2j0fi6WWgsJ3EBDw8pqMi0v3Y5/h7cavY2J5u6iKr8prKTCaufJQLmF6LWPcnbk+yIcx7seP6JGOiqidwo3aYrbx81upXcrHXRjJ0JnB3SYjS/5fCruTO0KGfZzVNBltmFr8SP/qbQCmzAxC4+RCfMhLpBXfBUCa6Q7Y3n5R+y5BWYXdOBQLnRg3+md+KPrkqC4ZHy34gBDP+E59e2h7t+LTE/Vbq2Rlc/Fm/pv8X7Lrszud21i0sddGiCDZ+xzoxXFRFPnzX2/oUp66N5tvV32FVTafE5O4SiMydlEEUSN8WfdhGuU5qfz6XjnZ++wiZ65ePUxj/TtHMUIUFM4i2tqKKSj8HyUln3Z7Xqv1xe5UYA8RvDhqBhcf48rwCJBVX8yeyiOktzSR0yZTadOxKHjYcfvUaDwBsNlayc5+FgAnpzAmTlh/3GvCnXQ8HRPM3yMC+Fd+OR+W1FBgNFNgNPN1RR0RTlpmebuxwMeDCR7OnSYVqX2O69M0U7ADKX01NUVtuKuG0dC+AuTnpkElQ82+CnZl1REy1IfQqUEOgamM91LZcYwBctX9I/GM9ACgqdZI3oYiJLNE/FJ7ZMygmIVYq5vIND7SqXsv4xxsUitN2v1I6jYkdRvZe//N13UbAAjVqbsYIH3iOB+OJEscqT3CxqKNfJv9LeUtdudWD50HtybdSmZdJt/lfNenLnVG+1ZC3u4C9nMYT2/30yK7XlFawxcffkOdqSP8OTQ4fMD77S+8g1y49IHRHPjVn53fppCXvJOSzDImXzaUhCmB54RBdSZRjBAFhQFAliRUmt4lwbLZjGzfMa1TmZfXFAID/4CbawJqtQdqtQvCSRwHoz2Cifbo+eSh0/kSE/0YjU2HsFqbqK5eh8XScPILAQ+Nmqeig7k7zJ/kxla+LK/ll+pG8trMvFNczTvF1UQbdFwW4MVNQT64qI+KyXdSmT8xuZugsD2528ZnEYEAYILreH6ufwCAykYLKn0DOmsxxuwIDh2pw7Aim8hIdzwCDGzdXelo7rI7hzoMEABXLz1DL4nu0m3IxCsI4Qramkuozd+Ji3ss7iEdfj47v1pKi08KFarvqLHqAZFrwpb08E31HKPVyO7y3Wws2simok1UtnW8F0+dJ0ujl3JTwk146D146+AJfC16SFN9A9+vXQ7AuKQZzF0yGZV6YHQx0g/lsnzFl9gEE8jg6xrKvIUzGTwkfED6GyjEdpGziGE+rPswjdLMNNZ/+AtZe4cy89ohivT7CVAcUxUUBoD8A/sIjB3Sq2gZWZZZv6GrRgWAv9+FlFc00NLSiIuLF3qdG4LgiSx7Mmz4H3Bx9gagrq4AV9dBncS2ekNzSxa7dl2ASuVMXNzTCIggCPbfCBicI3Fx7jphH0uT1cZP1Q1srG3ip6oG2tp1Rvy1ahb5evBJfhUmjcCkepkZ7i6dnhQNWhUXjQjCTa9m2MfDUMsy2wuKcermNlUqjSA14jWEGiNluQ14T/k3ev8jyJKIsSaK1toILM0+WFp8MDUGYm31Jt5Hx4ynJvXps/ktRXu+IrPpIdY2qFndqEWLwA9zvyFwUPffYU846hMyJ3QO00On80vBL+ws3YnR1hFh46R2YmLgRGaFzmJe+Dy0x2i+vHXwLV498CpBLkHEe9tXZI7d2gn5YQaaFgOS2kprYCV2hTL7eVWVQJtzEYhgEzv6c1Z58/dH/9Ln9/RbqsvqObD7CJmZGVQ2F4Ago5GdufTSS4lN6kN+nrMMSZJJ2VDMjm9SMbcVoHEKYtKlw0maFowgnt+rIopOiILCWYDZ2EbxkVQiR4zp9bU2Wxv19bupqPyJsrLlPb5u3LjdZGWuorbuCQBkWcTb636GDbu5V9LYra157Nh5fL1wQVAxadJ2dNqeaaM0WW18V1nPf/LLKTFZOp3T7KtGVd01W21HZ1bivb9gdbM9Yme5/1xaVE7EtuQxoeEQO2yJvOzxHHEeBuZG+tDaeDWCa1m3TcmygLpsNEmjH8A7bkSPxt4Tfti2nEeynkQWZB4cdC9Xz70JsyTRYLXhrFJh6KW43LQvp1FrrO1SHuAcwLTgaUwLnsbYQWPRqbqPwlieuZx/7vjncdv/046XTzqGkX/2oiG7gV0HNyOLNpAFlj3xeM/fxHE4vD+b77/7ARMNnbab3DR+3HTbNXj4nF/3+4aqVtZ/lE7xkTRk2URQ3FBmXZeAh/+phfGfzSjRMQoKZwGiqKKtsRFJsiGKJ1/GNhqN/PLLL7S2tjJ+/HjCwqbh7T2NyIi7qKz8iYbGA1RW2ifi0tIY1Gozfn75ndrYtatzsjtBkKite441az9i1qw1aDU9u/E5OYUTEnIjzc3p9nTtjtzsMvUNe5BlGxZzTY+NEFe1imsCvVni58EXZbVUmi28viUXwSIh1pi4ICEAdycNMjJf7S3ufLGsdhggAAdcYjGLWmJa7LlxhopZXFT3L5JNIbSE/Yzoan+eChAfQmttw2gpxGgrxixWYNQXUORWyJcbPyRhcxk3XTULg8uph1N+l/E9skrGX/bhslk3UGexMnlXOjUWKwaVyLrRsUQYet6Pk7pj2T7WM5aZoTOZGTqTWM/YHvkWLIlagovGhUZTIzKyQzb9KHUHbcitKgSthC6xzW4MiDKCCgS9hLePG+OShiIOExkxIZE3/vdfQEaSZMRTeIrPOVLIim+/RBLthqhWcMbPK5CRo4YzYkL8eek34e5rYOm9I0jd7Me2FUcoObKPz5aVMOGikQybHXJKn+f5hLISoqAwAJhaW8hN3otfWATewScOt9y5cyc///yz4zgxMZHBgwdTXV1Nbm4uzc3NtLS0YLN15N+YOnUqkyePY8vWvwNrOrVntQ7CajWj19c4yrw8H2XEiBtO6T1t3jIai6WOmJhlOOmD2LxlHeXlhbS2eDBu3OVM9GtFlfEjBI6AYVeCpvt98PTyRi54qSPB3OwhfkyN8aW6ycSHOwpYkDSImmYTeo2KVzKmn3Rc5b5aDg/puFdEDPqQyCH2hHHFG78jw/p3qk3uPLBlmaOOCrjEz50bFsQSH+fb68/CaGrjqe8f47tW+/eWEHQ/G1VJeKpV1Fk7vqenooO4Jbjn7d/2y21sL93OQ2Mf4qohV/V6XCdj3YdppO8oP+75m/81Bb2LPQSoqbGZ//v3vwB44G8P4+TSty2+1H1ZfPP9ciTBjJPgzk1/vA7fQd59autcpbG6jY2fplOQko4sNeM/OImxF4cQERd0pofWrygrIQoKZwk6gzNDJk2jvryM/T/9gId/AEFxCegMBurKS7GZzXiHhFFfX9/JAAFITU0lNbVr+KmHhweSJBEeHs748ePRag3Mmvk6zc0V7Nz1D4zGIqKi7mRI3CKqq/PZt/8C1Gr7k2dt3ZOUlAwjKKjvWxFHn1cyM5cB4O5u/5EkkQ0b3LCo3+VzLxdmZn1DUO1hJs5+HrXY9RYTF+DGpMHebMu2G0m/Hqnk1yMdzpa+rjqevdgu8V7wWCBhYinVNj+shlgQjYCMa0sGzqJd2Mu12dapfS85i8q966gtb6XE8DWIkNfQ2RC0AV9VNvDVB7v5fOlQJowP6fHnUFlVxp9/uI1MlV0XJNHpAjaICQCdDBCA5eW1vTJCjn5ex66I9CeRI/woPFyLxWRDkmRkm32V4yhbvspk9o32lQm1pmMV78UX/kV0SAJhkSFMnDWyx/2t+3EbW/esQxYkdLhyy59vxNvPoz/f0jmBm48Ti+8azpHt/mz96ggFJWvI+LAVnbs3t9/xZ1xdf7/ZeZWVEAWF04AsyxSmHsTc2opnYBAanZ6qgjxq6+tYvWU7dLNtM3Kk/WavUqkICQkhISEBlap3UQqrV89Eoy1CFCWcnW9n/Li/9vk95Oa+RHn5d4gqHaKoo6ysBBeXOgB27riGiz/+ylH3yykiYTf+jRsm3NDtUrvZKrG3oJaPdxQgigKyLJNZ0Ux2ZTNXjwvl6YvsRsjc+34iUysx3qjmi5fmOa43Vlei+W8cqvbsrKWBMaRH1iAfZ4nb78jV7G4M5smGrk+ecy02ptYXM1jVSOJNCzAMj0XUd95Ckaw2jEdyabCauXn/PRSpy3G1OXNz5IM8QQTm9tvobV4i4sFPkQQVbwXbk8AlT4xnkK5nqwh/Wf8XNhZtZNmEZVwSc0mPrukPvnspmeJ0+3ep1aswuOsIjPFgU9YX3dZ3EX0JDQ5r9+0QEAShwwFWsDvDVtdUUdaYA9j9Pm6943pcPXqeEfp8pbnOyCdvraSy4QCiyYjs7sWCRYsYOXLkOb8tpayEKCicpQiCQFjS8E5l7n7+SJKN8tJSsqrraWpq6nQ+Pj6ewYP7FmlRUFDAhx9+iCRNdZRdckkEVqsRtbpvIkqRkfcQGXmP47ioaDeZWVcCsHTxaEzbvkBTIiCaBP6wReIbj3382hLMnDlzurSlVYtMjPJhYlSHb8nLv2bxn18zWZlcwshQT2TAPcQFKhrZqbdiNlvRau23LEt9Nfp2A6TE93oCb3kJcfeD5NStQBZFTBp7RI7K5E7goT9hqBvCPKAYI+9jdvTpaWzkz788h95mLyva/CEIIoLBE5WHH5qQSGw1lViKMrGaGnjmDyJFESKeNjfen/UuN5aDudXISDcDq0ZGIzzhAdhjTlb6zqJS581LeSU8H9ezqI+KlgoAJHtu4tPGjGvi+OmtFKqLmjEbbZiNrdRXtOKpGk2d794u9ZulKtIKq3rUdqhnPNfdfjFqjTLdALh46rnp3kt55tkMJK0OVWMdq5Z/SWpqKhdeeCGenp5neoinFeWvQkHhDCKKKpKGDuPCEaOxWq089dRTjnMZGRkEBQXh5NS7pflvvvmGQ4cOdSlfseIQWu1OAgJqGTfuCmJiJtDW1oyLi2+vImiO0tjYka+lvPoe+Jv9tc+LaoqIwKb3ZNu2bahUKsZNGIdVtMurH33aE+h4ehYEAY3GDIKJVovAX7/e096y4HjaXr+tmAtmhNtLNR0rFb7X/BNBFAkY/wIBvACAZGmhMfU7pG0mrC3QWpWBoFJzvbmVQwGR7MNuwDy852OHAQKAqAHJgtxSg7WlBmvJEQDManhjsUhKhIjeIvDWnLcICYkhM9f+ORtEkSdySpGi7mBEYzoz6naR2JbBet1EdjcePwLIKlk5UHmAz9M/Z174PCpb7dtSjabG414zELj5OPGHR8ZiMdtoqjZSdKSW5LUFtDSAd8VEQObCv4yixVTPpvXbkNvDruVj/oXOaqsCAgkJiUxbMOacf8Lvb7Q6DWr0WAUjofFjKM4/QtHB/bxRWMgFCxcyYsSI381npmzHKCicYfKS9xIxYjRg//t/++23O62KjBkzhgULFvTopvTdd9+RnJzcqWxIvD91tflUVmqQpO6fOyIjJcaNm0pk5GQ0mp6tlNTVFbBj5yWoVC2oVGaH+JjJlMSC+d+yatUqdu3ZRYVTBdsDtp+4sR4w1jybZ/ziIH8bsiQT0PA9AE3X78U14vjaJbIsU/vMfVR+3OF7I3z+A54BntRW1dJUVoVBpyZ0aCwu7q6Ycwox55fQtG47toYaGnz1POr8NTmDBERJ5invm1l84b3UWawM2drVd+e3hDtp2Tm+q4pqs7mZCZ9P6PaagdiOkWUZU2sLemeXHtW3Wmys/yidrD321ZnQeC8W3TnsvNe6OF0898T/YZSbmDVhMQljIli5ciWlR1KRVSoGDxvJ4sWLzzlfEUUnREHhHORYIwTAZrPx66+/smPHDkfZ3LlzmTDBPmEJgkBxcTGZmZmYzWZ27tzZbbvXXnstUVEdmu4Wi4k9e75g7drjZ3o1GFq57LIlhIdP7PWT2M8/X45Guw+TMYkFC75FlmWWvrqUXLf+SUamlmXWFZbgJXVsVUiyiOnOIzj5BpzwWtlkJH2Y3SlX4yoxeE9Gj/u97NWJpLs24WIUeD7+AaZOuRqAFpuN8TuPUGW28sdgXwQB3ioowdD4PaLUiiQ6IYtOTPT05argYFy1rjhrnHHRuGBQG6gz1XH16qu79Oeuc+ezBZ8R6tbzJHYnoyTjCBs//B/lOVlEj51IUFwCvmERhCQknfB7liSZXd/lsn+NPSx6/p+SiBze+2giha48vewFLLQyYegs5l08BUmS2LFjBxt+/glaGtF4+7H4oouJj++HNACnCcUnREHhHOS3zwEqlYp58+Yxe/Zstm7dyoYNG1i7di1r164FYMKECezbtw+z2dxdcwBceumlnQwQAI1Gx8SJ1zNxot0gaWqq4uDBlezdm4VWq6euzkBrq4EPP/wFZ+fviYjwJD5+LP7+wahUoNP5o9e7HXfScnGJw2TeB6iRJInPdn7WyQC5yv8q/jb3b47V+2N1LGS582vHZ9NedtP7I0jT6fjJxcB061Rs7pGIooAmfDj+JzFAAFp+eN/x2tIkYivJQhV0YuXX3ftX8eTOf5Lv2grAWyOfY+iYBY7zzioVKZMSO10zVZPOPRs75245VA+Hjm/3OXht1muM9h+NRqVBI2pOfsEJyNy1jZ0rvsDU2orZ2IaxqWN7J2v3drJ221emfMMj8fAPoLGqCnNbC1GjxzPtmpscdUVRYMJFURSkVlNT0oK5rWvGYoW+IUk2EMHX3x6uLIoikyZNYvDgwXzzzTdU52ax4v13KZl7Qbd+VecLihGioHAGMRvbUGu7j5xQqVRMnTqV1tZWdu3a5SjfsWNHpyiZwMBA/Pz8OHDggKMsLi7uhP1qNDq8vIKZMeMvzJhhLyuvyOTble9TUaGhpcWJ1FQjqambO11nMJgYNiyEuLhJhIYO7WSQyLIFswQ/1pSy7KPxtAltjnNfzvmS+MC+P9EtcookTSrhexdnrrrlU4Re+rAY5l+N/uWXMFbZryu8aimhy39G5dd9aO5d717LBvUBaA/mGGzxJVtlJDv5GxBgfcV6LIIFo82I0WakzdpGm7WNJnPHNtpVcVfRbGmmxdJCs7mZZov9p8nchMlmwmQzYZXsk7q7zp1En0QMPRSVOxGyLLP184+oK+tICCcIIokzZhMzYQrFaSnUlZaQd2AfVfm5VOV3GIp7f/iGsMRhhA8f1alNncFuFDXXn0DhVqFX2AT7Q4SXr3uncn9/f2699VZ++uknDmzewI6tWxUjREFBYWAoSU8jJD7puOcFQWD+/PnEx8fz/vsdT/PHCpcFBgYSFBTkMEKio6NRq3v/XzvAP4Y//elZmpvL2b59OYVFxZSWdPYjaW3VsWNHJTt2rMTb+ysWLVpMRMR4AF4p20F6mwFo6iTLvWLeCmICYno9nmNZtOQd/v3NBaTpdOTkrmHw4Pm9ul50diNiSwbGrasovOOvGCskiq5YTMg3v6LysEfoWJqayFi3jg27d2N0d7JnyWsnW1PF4yk9ly6/OfFm7hl1z0nr2SQbZsmMWlSf8uoH2J+uf/nfqw4D5JKH/4mLlzfOHp44OTuDqZHwofZtqdbGBg5v/BVjSzODBseyffmnVBXksemT97oYIVq93eg98EshI+eGOrITK3SlqaGF0mK7IJwoiPbwZUEAWaA4r4LiolJq6qpAsK/yFeaVEB7TOXRcrVYzdOhQDmxaj1bft2i2cwXFCFFQOIOY21qPuxJyLGFhYdxzzz1s27aN/fv3dzJCTCYTJSUdT71XX93Vz6A3uLgEMHeuPWGZLMtYLBYkSaKtrZTt21eQk1NBXZ0TNTUaPvzwJwIDVxIaFoBnSTBDBD8AKpwqqNXbc6C46k7duc7TLZgpohsb5Ca+OfgO9/fSCDmKfvJCQl+TKbzjb7SVmki57ELyxs2kxmiiUqPGrNOBVktgWyDT60aR5laEFWtHJE+7ir1kk1DJKu6deS/uBnec1E6OH2eNMwHOJ98iAlCJKpzE/hMmq8jJJnXDL45jV29fvINDsFosZLx1J6s3FiAh4qdrps7sBBZwMZrZ7axn0Q1X8uOHeVQXFVBw6ABhQ4cjyzLVxc3UV9pXtUytViwmGzqDYoT8FlmWWfnhLxzK2wlCD0OsZQFXt+4dhUXRnjxSPM+jZBQjREHhDGGzWmhtqKcyPxe/8MiT1vfw8GDhwoVMmTKFzz77jPJy+9NWYGAga9bYpdv724lNEAS07UaSXh/JwoV/B6C0dD8//PA1ZWVaSkudKC1tIJDBBLZfF94Uzk+hPwEw9/u5zHWbi4iISlQR5h7GrVNuRa068e3nh8Mvk1J1gMnBsxkTupiFYXPYkP8NHzdnEvT1BVx18Y8I3Siyngz95EWEvFBP9oPP8e2kufZCF3s7OrMZJ42GJUuWEDGyQxm0oaGBDRs20NbWhtVqJSfHLsI1O2R2r0OoBxJnj84aE7+8/SqjFi1l/btv0FxXC4i4qD2YEfiAo46tqRzToS8w//UZwgK9KfBxZ8Wzy4gY/QgtDVYaKju21aJG+jm2ZhQ6sFisfPzGCgprj9ijyiUNoqxCFtqt1nbvJo2oxd3FG18fP4JDAomOD8d3kFe3bba0tIAs9+gh5VxGiY5RUDjD5CbvQe/sSmDMif04jkWSJFJSUrBYLPz444+O8oCAAKKjo/Hy8iI8PBxZljly5Ajp6el4eXkRGBbI0IShOOn6Z+LMz9/Llq0/0dZqBNFCSYUGwaLBJtj4Luy79ptw91zhdwWPzH+kU5ksS/ya8SbvpX1KWmMzIc0hmEUzba7lBOlcOdzaCALMbmnlMWcfPK7djtDDJIGlGfspSl5PfmkVVWYdzXT4X3g3NbHgiisIHzmyW1XaLVu2sG7duk5lzs7O3Hfffb1WsR1oJJuN6qICPnnoHoeeB4BOo8HJ1EiwlysJnp2VcyVTIy0//Y02jZoN8WGACp3HXXb5dq2IT7ALwXFejFkUoSRe+w2FuSV88elyWm31AAwJH8Fl1y3uk/bOsRw5coSv33sbSefEpOkzmDx5MvqzfGtGCdFVUDhHydm3m6hRY09e8TfYbDaefPLJXl/nm+jLHZfe0evrTkZeXh4ffvghAHf/7W5eXP8idcY6JFnCJtvYZepwsHW1uXJR8EWIguj4+ajkbcyygMFiYH5x91suBS4FXGpMY6E1nRZvX4xBsXD0NiZL7a9lBFlGlmUEZF45MuW4Y3YVjfhF2J82ZWSQBUBGrw9CpxuELMuUl5dTXl6OIAgsXLgQV1dXQkJCMBjO3rTsL19zMVaL3flxzJJLGX/R5dS9/F90kyfT/GPn7QJLwTaMyR/SMGo4O/BC1Eaj1g1lyMRBTL48Gq1eWTT/LTarjW8//5mU7L0gyAiSmomjZjBnyaR+ad9qtfLJJ59QlJYCNhu+0XHceuutZ53ReyyKEaKgcI7SVyMEwGKxUFBQQH19PbW1tRw4cIDWVntYqSAIGNwNNFobqbJVEdDW4avgOsaVvy7sey6Z7jjWCFm2bFmX85Ik8cLaF/i04tMTthNfF8+Q+iEnrHMrnxFERY/GtYx7e1TvZEyePJnZs2f3S1sDTWHqQda9+wZjl15GwrRZPb7O2Gzhm3/to668FXc/J675Z/eCar9nJEnif//5mPIme+y1i+DLpVdcTHjsoH7tR5Zl0tPT+XbF11jra1l6/U0kJR3fkf1Mo+iEKCicg7TU1+Hk2neDW6PRdMoxM3fuXCRJosnUxO0bbudQVYeE+7iKcQS3BgPQtKeJ+1Pu59n7n0XVgy2NnnCyZxpRFLlz+p0U/lBIlbHKrhQiy0hIyMhodJlUWARGhR+m9cAQBMGGLHc/tkPEEUQF9VHD+aY6CBEL833r0KkAQQBBQBYEQOA680FSGwdh8QkAlRoER7o1e+TCMccWaz3NzWmIgpbIyLscdTQazVk9AQCEh4dzzz33cM899xCaOIwb//Mmw4cPZ+nSpd0ahd2hd9Gw9L6RvH//Vhoq28g7VE3EUJ+TX/g7YvVXm+wGiAzDB09i8ZUzUan7f4VCEASGDBlC+aTJbP3xO9auWYOXlxdBQV0TMZ6rKEaIgsIZJn3bZkbOX9yvbQqCwOSvJgOgU+mYEjSFaM9obkm6ha+2fkX2hmwADEYDj776KM/c9Uy/9n8iXPQuvH7Z692ekyQLgqC2GwZL7WW5ubls2LABi8ViN1gkIxrtTqSAArZ5PcQzu6M4XGHfFlnWvjCiV5lw0bbi52IBWSIxUMsj18/H3fnkk2lraz47ds5CpXJhypTjb+OcDRxrdBzlxRdfpL6+vsdGR3cY3LQExXpQklHP6tcPccPzk3B21538wt9gM5kp+Gw1LTt3IVmtyDYZSZIJuO5yAmeP6/P4zhRWq5VvP/+Z1Ox9IEBc6AiWXjvwGh4TJkwgNTWFhrxs3n31FYZPmMS0adNwd3c/+cVnOcp2jILCGaa1sYHitBRixk/utzYnfT6JRrNdJfPVma8yLWRap/PFtcX8593/4NpiD5+97s/XEel/8gidk5GTk8PHH38MdL8d019s2boMs/ljLBYdf9rwYq+udVIbCXWvYXq0FrUgImeXEleby+hpw/GdcCUWVSPrV91CW9VQBkffjVojotGpCBnihbNH7yfigSQ8PJzGxg411Lq6OgBcXV0d/ioVFRVoNBoefvjhXn0nNaXNfPHP3QCo1CLxkwYxYl4Yrl4nd46UJIn0f3+M5dO30LbVdVtH++DzhFzasbUlCCIa57PX8TJlbwY/rvoRk2wXpPNyCuT2v97UJ02evtDS0sLatWtJ2bUT0WzE6uzK7HkXMHly/903ThXFJ0RB4RylIi8HjU6HV2Bwv7T3QeoH/N++/wMg5fqUbuu0mdp4/tnnATAMNnD/Nfefcr+nywixWE388stUNNoabv3lZUf5uOAK9GqJUC81c5Pi+XjbIdZm93yf/rOfluE+35uDhYuocx0Cx2g0+Ee4cekDo09w9elHrbZL5MuyjJOTE21t9nDao0q2BoPBHuoJPP74473+TorSa9n1XS4VeXZDRxQFYicEMHp+OG4+3UdYleXWU3jXfbhl23MfmbWumBImIbi4IpcX45q1o9vrACRBRciqNbhFnj3bDZIk8dEbX5NfmQYCiJKGEbETWfCHqQOyBXMyCgoKWL16NdXZGUhaPXfd/wCenp4nv/A0oPiEKCicozTXVhM5sm+Oqd1xUfRFDiNkb/leRgd0njxzK3L56I2PHMdtqjb6g055X2R5wNKRa9Q6oiL/QXHJPTw7+Z88uu1hrLKa2mYzX95+FV7tN8ApQ+yfaXVjNcU1ZWzNyOBAUQOCoEGSZawtZjaVBzravWr+Mlb+8BAjbK9hjFSRtXAaxvpQmopGodaenQJd3T1H9tezZUicF8GxnpRk1rN3dR4lGfUc2VZGbWlLJ4NMlmTyDlVz4NdCLDs2MbTdALEsvJ4hT9yN1qXDYEl/6ROkN59BoOsYRdlG5l+XMXrl2/0y/v7gk7e+Ib/KboD4GsK47Oql+AWduUk/LCyM2267jWeeeQZVXRXffPMNN9988xkbz6miGCEKCmcYi9GIWqvr1wnbXedOkEsQJc0l3LjmRqYHT+eCkAvYv3s/1hYrlkoLWuxhqR5DPLj78rv7re+jDKQRAhARNpsDaz3wS6jmrpFv8dqBm8mqD2HK02t59oJwLpzRMUn6uPng4+bD8IiujqW1RUWMfK3Deff9+AX8OeU7bCFmvIf8DEBT6DBiR41FkhIQxbNnS+Zkn6/N2kPlzpP0ERzriX+4G/+7exNgj4LOSa5EssqY2qykbiqmpsS+4hLd1JGLJnRyHBpD588r7p5rMN90EZKlIxlexfrdtD5qdwJ2PrL1lMfcX3z32a/kVqQCMCpuCouv6HmU0UAiiiKTJ09my6rvKc7Jpri4mODg/llFPd0oRoiCwhkm/+B+okb3v5PeH4f+kce32/OdbCzeiHWPFX+jPwKCwwBZfM1iRg0edaJmzlq0eidmXvI9nz54C/4jqrg16EM+K7qMWrx4ZHUui6eNPGmiuw+25bHsh7ROZXGJaVQusmAN6nhSdw06SGn5QcoqPmTmjCOO8t8aWrIkI5tsCHrVgBpgRzlWEKu7/izW42da7i1r30l1vK7Mb+Tnt1I7ndc6qUmcGkRE5Sjq/mMXdit76CE0gwJwHj++c103507HToH+tB5zfHhIInbpUZAR2l/b106sendC/vcWPqPs4n7GFgv1la34Brui0vTfatXGVbtJztgKAkQHDjtrDJCjTJkyhY0bN6JuqOGLzz7jb/ef+nbqmUAxQhQUziAWk5GWhnrEARAgujj6Yi6OvpjchlzeOvgWcnHX5e9136xjr/tePD09WbBgAS4u3eex6Cm/3Y4ZaHwDg4iYvJjsdatBFPjgwVFc+HkeTSpnrnz6S16/+0K8fjPhARgtNn44WNrJAHlsUTxLkkTKiiOpqDjcbX9GWc+Ve9LIPPwcnzy3B4D1kxKoCkkEYMavRxgzagfFQx7EbUEo4jEiXzaLlcqcWjRCMDarFZvFitVswtzWhpuPL1onJ7xDwvAO6j6zb3c4OTlhNh/f0Dg2x9CpIkkd36ezhw5XLx0qjYgoCvhHuDNsVgh6Zw2yNQyxrYH65V9jq6mh7cDBLkbIb/GZkEhhyFCci+wrUqLcPu5u/oTULZUUPPsyFSNGgCCQsasCEKh3j8RpcCQ+wS54B7vgE+yCT7ArBrfeyZ6bjGa++uh7ckpSQQB/twiuvGVJr9o4HahUKhISEsjcuoGWxgaSk5MZMWLEmR5Wr1EcUxUUziDGlmbSNm/A3c8PUaXGzccX7+DQAekrPT2drKwsjEYjlZWVNDc3OxwZjyIIAqIoIssyer2eESNGMHv27B4/1WdlZfHpp3Yhsn/84x+nJXLAarXw3z/dgNTUgHNYFFmBk1hRZgBBwF9u4uPbJhET2TGxb8yo5MEVKZQ3Gh1lb1w9kvlJg45ps4nde5bS1pYPQHV1MM0WD/4x6HFkQUSUJNbdcTXVejfWzp6Dqj33jHvNMP6QdzdH7lV3cmoFKN3lS+WBE4cIqzQa/vTWx+ide2YMjh49mn379vWobl8cU49FlmUsRhsqrYiqB1l086+8irbkZAAivvsWXUzMSf+OjFX1GGsbARlZkkGWkW12FVxZlsm790GcS9OOe32jSyhGJ2/S4q5FUtm3gZzctHbDJMgFFw8demc1OmcNehcNemcNemc1Ta0N+Pr6krw9nZ/W/oiZZgD8nMK59b5r0GjOzuf1pqYm3n//fRoK85B1em647c+Ehg7M/aMnKI6pCgrnGHpnF0bOX4wk2RBFFfXlZeTs24UgiIQkJKHR9V/IYlxcHHFxHflprFYrRUVFbNq0ifz8fMA+0Rx9em5tbWXbtm1s27aNu279Mx6DfI+bD8NstaFVq3q0EiJJVlJSPqCs/FMkqRGdrh4AH5+HqK0txmjMIihwKUOHXtaj96VWa5h98+2seelZWgpy+MPcBUxL9ODBtUVUiK5c8+YWVt49i6Agf/bm13LD+/YVjCAPJ64cG8IVY0PxcdH9pk1XJk5YR3ZOLi99uIJ6WY+n0Io8yD6JSqJIsl80D0+8jWhVFZOwf362qvf43HkowrsSokZC527GNagVQZA7GSCRI8egUmtQaTSIKhWN1ZWUpKdhs1g4tCGTA0069mtshIzwwU2tQiUIaEWBhb7uDNJ1PNnPmTOH/fv32xcMTvA8eap5TKA9maFTz6cM9yVLHEZI3pKlBP7rX7gvWnjCa/S+Huh9PY57vuXuu6h85wOwWe3vV5YRrSaciu0rV27Nhbg1FyJPWUiJJor6ylbaGs0UpdVSlFbbbZtVAZu7lImyhokjZjLrwvEIZ3GuHFdXV+68806++eYb0rdv5vvvv+fOO+8808PqFcpKiILCWYgk2cjcsZXYiVNPi2+BzWajpKSElS8cAFlAFq3Uex90nJ8y9WNsiKzkMn5mMeENVkqSG/ntToCIhAqJcFUdqx6/ypGB9ygHNx4hK+sFkEU8ojb+drGgEybTKC6Y94VjArXZLFRVZaLRBDg+E0EQcHd3RxRF3n30fuoz00CjY+oV15CeVcgzpYG0qTryu2hUAhabjKtezZ5HZqPXdL8N1mi0sHxvMa+uz6Ku1eIoF9qzoUpOaoQ2m0NldXLNNkY0Huq2rd8y4qKRzLzin53KJEnmP1f9AeRWtK7XIqp9AfjvQnfqXVSMbDzMt/teQga0UWEIBlfMpib2Oflx5f2rKMgt7LavmAhPCsvaGDduHBs3buzR+PoL2Waj5N77aNm+Ham5Ga8bbsD/wQdOfmEfsJSXY8rKpvyJJ7AUFxPyzju4TJ6ExWSjprSZmuJmakpbaGsyY2y2YGyx/7S1WCj33NipLS/nAK6+/g94+50dYa89oaysjLf/83/gZODRZU+clntGdygrIQoK5wmiqCJs2Ehy9u5i8Jju99PbmhoxtjRjNZmwmExYzWZsVotjYpQBJxdX/CKiEEQRyWZDdZztEZVKRWhoKFOXwPYV2WADz+qR1Pnsd+zL/5f72CNMALONvF3dP1VKiEiIZNt8HCshsixTVFTEtk/3MaHem0jrteypz6RkewKDZ+9E7ZuJStWExeqELLuh09llT3W6jm0Gs6WVLVvskS3l5VFkZU7s1O/jjz/OxXf/nXfv/iOCxcTmj98F4GKNJ58GX+GoZ7HZx7TizxO7NUAkSSavpoXF/91Kq9m+IqTXiIS5iuTWmrBgv0Zss5/zC9Cw8papqGsS+eT+joSAOtFA1Px5SK2pGFtakJGQZRs6g54x827r0q+x2cx/w68H4NaGRlwFI1Z1K76NAla1jtuzt1JsfgEtajhig/ZxHPTYTOJtSai+VVOVUoVB78LTy57g36+/xIwZM3jppZdOm5jWbxFUKoJfeZmyRx+jfvlyaj/4AK/rr0MzqH/zqwBoAgLQBAQgOjm19203XDU6FQER7gREHF9ZtKlxNFs3b2PX3nZdE6GZ4rJCPH3c+2UF6XTQ0NCAINmwCSLJyckEBAQQGBh48gvPApSVEAWFs5isPfYbo1qjRZYlREF0+OrpXVxwcnFDrdWi1unQ6HSIKnWnp6DWhnoqC/JAkhBUKmwWC5IkIapEnD288AwYhNapcybY5c/uobKgqVNZ3OW38jAvUiBEospvQpPRodI5eZCV4QEGYsJC2JRezor0FtTYOPzEPIxGI99++y2luUVca+pQba0zVbC29INu37NbWBMhU8rROLeHcMrTQdjoOC9JInt239jJIfOee+7Bw8ODLT+vZtuKLxAlG+46DQCJV/yJq1fXAPCH0SHcf0Es3r/ZfimoaWHaixv5LX+ZOZg/T4/CoFVjMlsorKzDbJXYm1VCfKgvY2I7778fOngE988qUAmdDZwmsR6L1gLIyMKxt1wBAQGrJLDZ5Mb7mGhA5gb9nm4/m5GWCEbaOpRtS8U6Vmv3AzDcYywTpsXhP+LUlW/7k+YtWym69VYAQj/8EOdx/aeH81tyFi7CnJODyscHW3U1rnPn4nnN1TiPPXmfZWVlrFixgurqagDc3NxITEwkKSmJgICAM7a60BMOHz7Mio8+QLBa7FFEGi0xw0Ywf/780yrtriimKigo9AhZlmlrbKC2rARzayuCKOITEoa5rZXKglIKD1cjy85QJ2HZ/Cs+Y1ZQGmXgRflhtPtqHEJTMgJl+kFYRA1+1FFJxxL2e3Od2LzZvt/uJblwsbkjDLmw+Qg7qr4/7vh8x9YTNKKs23OiqGfG9MOdnCyPvpZlmffff5/CwkLi4uK44oorum3jtzzyzSE+3V3kOPZG4I+DvJlaacbVBmsSXLn52uEYLTZ0avvTcWZaFcU/5BBbbyUrzo2pVyeh0ogUP7ilR30ej61YSNd39VMACFZ5MrstFLXkg4zMF7pttAgmALyqxuBVU8KisJV4jPKD2cvAK+KUxtJf5CxYiDk3l7CPP8IwZsyA9VNw7XW07ulqwA1JP9JN7a5YrVa2b9/Otm3bMJlMjvKgoCAWLlx41q4uyLLMnj17yM7OpqWlhbLcbERTG4KXH5MmT2b8+PHo9QMvia8YIQoKCn3C2NJM0eFDNFZV4tUeItpUU4XVbMYzIBBJkkjZ9AahHk682ziPVeX2JX5BlggylqKWrZhEHRU6PyRBhZvQxsW6Dh2JKy9aQvp/vyHcNZH91b9SZy5v964AvasLTi6uSJKVxqoaoqcOw+SdgLvXJ4hi18lDo/Fi0sSdPPnkk46ySZMmUV9fjyiKqFQqDhw4YO/3yiuJjY3t0kZbei0Nq3KRTDbkNis2iw0RgSZksrExoo871ZV6ET+jXSAsRzbjH2nDZrRgaTWi83EFGQRZBrn9qbp9VUSdI6MXO0KJd6ozSVUXdWl/siWOOFsQAo1Uat9jlRiHvjEGldUFrdmToJItTHF5C+/YFpjzT5jU/yJ0fSHngvmY8/MJeft/uAxgUkBLZSUt27fT8O13tB04gGy0R0BF/foL2l6IeVksFrKyskhNTSUjIwObzYYgCMyYMYMpU6ac1asiACUlJXz20UcYqysQkHEKCGLy1GnExMQMqMS7YoQoKCj0CclmI2v3dmIndEwQsixTX15KfUX5MfWsiCo1fuGROHt40mS0sPLLL/Coz8DY0swO3VhE70FM9DaiE2yYzWYSEhJQNzfw7QsdzphjllxK+NARBERFd9kO6ujfxvoNMY5jd/fR6HR+BAddi6fnWJYvX87hw93reRwlIiKC66+/vkt5T1YrSpDwRMBA7yccKzK/DnHllut7ptsgSRLFD29FbO/Lgo2d6kz2OKXjYjOgt+kJk3yZahmCSTDzUOgrvFe5GVdZpsYSyhc1LyNINmbkPELcpAwEEVj6Bgy/qtdjHwhy5l2AuaDAfqBWE7NtK6oB3iaQJYn0+AQAIlf9iC4qqk/tNDU18dNPP5GWZg8NHjNmDPPnzz/r/UXa2trIyMhg06aNNBXmIQsCss6J0ZOmMH/+/AExpBQjREFBoc8UHT6Eqa0Nv/AI3Hz8jltPliRKszIwtTYjIKDWaglJGNqpTv6hZGRJInzoCIdTbG7yXmTJRvTYicdpuStmcw2NjQfR64NxcYnpcn716tUcOXIEFxcXEhISaG1tJSsri6qqKgIDA5kwYQJJSZ2l2tvSaqj56PhaE5mihPOUIMbOi0IQBapKmzi4q5jIEHf0bjpS9pbhFeRCdKIfHt52A8pktrFheRrW/EZUHjr8RgUwanzvZLTX/+cLYiq6Jm7zfWIMvxatY/2q7zHY9OxzSaNIZzcME00mhhtNCIKI1jkKm8qIrq6QS5uaCbjiS4ge+DTzPaHqv69S/dprjmO/++/H+6Yb+9SWZDIhH/UHckykwjEvBcfv7JmzsNXVEfnD9+iio/s2+HZ2797N6tWrAYiNjWXhwoXnxBzV2trK9u3bKSoqoijjCIJkY+K8BcyYMaPfDSnFCFFQUDglWhvqqczPJXzYyF5fK0sSjdWVlOdk4REQiOegQIoOp2CzWogYMRqN9uzIuWJtMFH9dgrW6jY0g5yxNZqRWizHra/2dcJpqC+GYb5o/LpftekPPvr6DYYeCMbZpsdJ7ti/931iDIfqUrhpzU29am+wx2C+WvQVGpWmv4faJ2SLhfThI6Bdh2bwpo1o/P171Ubr/v0U3nSzY5ulp0R89x362K5GbG9JSUlh5cqVSJKEVqtl7NixeHp6YrPZiIiIwNfX95T76E8OZBWxeW8KN180C2e9jl27drFm5QqQJeZf+gfG9LN/jhKiq6CgcEoY3D3QGZzJ3rsL/8goXDy9uyzbSjYbFXnZtNTVodJ0THCCIODi6dVpSydq1FgkyUbBwWSsVgsanZ6Q+ERU6jM3MarddQT8rXNW4ZrPjtB2qLrb+taqNprWFdK0rhCPJVFoBjmjC+//rYRiynkx+nXmq2aw0GUOrvslXG+IRqfTMyZgDBcNvoiV2SsBGOsUiH9jBX5e0bzblN5te9n12ewo28HU4Kn9Pta+IGg0BP3f/1Fyzz0AZE+bjsrDA+9bjp8BVjKacJ4wHqeRIxEEgbaDh3ptgGiCgtCG9E9yt6SkJHx8fFi1ahXFxcVs3dqRbE8QBEaOHMmMGTNOOf1Bf7B83U4ObF6LRpB462sb912zGL1ej6Q3oGqqJz8/v9+NkL6grIQoKCh0QZZlKvNyKEw9iHdIKIIgHnNOIiAyGoO7R6/btZhN5B/Y16stmdOBZLbRdqgKqcVCw5p8dFEeiE5qzMXN2Gq7Tno+tyah8TegculdXpITceF788lTFfNk0MMsnX1ll/P/3PFPlmcudxxrRA1Tg6eyrnDdcdvcddUuDJqBW73pC+VPPkVdu7R/b1D7+mKtrgZZxnX+BQQ+/3yHSuyxv39TJuj1CP2cm0mSJFJSUsjJycFoNGI2mx2qw1qtlsmTJzNhwgQ0mtNvbEuSxEuffEdjbofYYIvKhYtmTeSXVT+iam1CFlVEjBjNNddc0699K9sxCgoKZz35B/cTGDsErd7pTA+lR8hWibJnd3fdshFAN9gDXaQ7LpOCkNustB2p7SSfLuhUGJJ8EI6jzHqUkqJ8Lli/GEEW2LhkHV6eXZf1zTYz1/10HYdrTuyMe5TBHoNZuWRlj+qebqx1dVS/+hpSa+tx65gLC2k7Tl4c/8cexeuqs8Pp9igFBQWsWbOG0tJSwK4zMmfOHBITE09bNE1TSxsvvPURqkZ7eHuLayjOTYUgy6ham0AQ0Pn4M3XqVBITE/t9xUYxQhQUFM56ZEkia88OYsZNOtND6TGyLGMubKIttRpzYRO2BhO2etPJL+wGQa9GNlpxHhuAoBZR+xu4J+MBdsn2PCuPjHsEsX3lSRDsYmbFTcV8nPYxZqlDoC3AOQCjxUi9ub5LHzcm3Mh9o+/r0/jOJo6NcAEY9PRTuM6ahcrD48wN6gRIkkRqairr1q2joaEBAA8PD3Q6Hb6+vlx00UWoBiBjNkBecTn/++BjdNYWbLJAli6G0Jh4zAdX49NUgs3FnYVLL2LUqFEDZhQpRoiCgsI5QX15Ga2NDQTGxJ288lmKuaiJqv8dQrZIncq1Ia6oPHS0pXTvY9Id84fc3uv+P1/4OYk+idS01fDY9sfYXGwXOFOLalZdtIpAl7NTWKu3SK2tNK5diy5qME5JiWd6OD3CYrGwbds2Nm3a1CmR44wZM5g2bdoJruwbm/emsObH79BgpVXWsN48GF1rAyrZhrvQRqSbmdlz5rB46qh+7/tYFCNEQUHhnKE8O5O6ijLcff0IjBlypofTZ2wNJmzNFhBA5aZ1+InYGkzUr86j7WAVal8nrFVtqL31aEPdaE2udFyvj/XkRfF/rGYD0R7RhLuHI8syMjKSLDlE3XaX7WZu+FwujbmUYb7Duo5DspFWk4aXkxdBLl1DfRVOP3l5eaSlpZGXl+eQg//b3/7Wb9sgsizzzoqfKUrZhShAlVVHRqMLsixT6xLMpZPjSAxyZ9YQf1x0Ax+HMuBGyBtvvMEbb7zhcMBJSEjgscceY/78+Z3qybLMggUL+Pnnn1m5ciVLly4d0DehoKBw7tJSX0dpVjounl74RwxGluXjJto7XzAXN4EooA0881EUCgNPcnIy3333neM4ODiYsLAwnJycOmWEPtHr7s69snIr4UI1qpZGSiR30qQAGjzCmBwbwF/nxBDpe3r/vgY8RDc4OJjnnnuO6OhoZFnmww8/ZMmSJSQnJ5OQ0LFv99JLL531srYKCgpnB84enkSPmUBLfR2FqQdBEJBs9uR1rt6++ISEnXf3E22w65kegsJpZNiwYWRnZzsUfouLiykuLj7ldsNVIDY2sF2fRMLw0dwb7cOCpEHo1APjdzIQnPJ2jJeXFy+++CI332yP9T5w4ACLFi1i7969DBo0SFkJUVBQ6DONVZVUF9vlvgVBxNnDE8+AQDSnIRmXgsJAUFdXR0FBAcXFxVitVvvWW/s0fPT1b4+7e221SazPrKGu0cobT9xOoMeZjzY7rWJlNpuN5cuX09LSwoQJEwC7POxVV13Fa6+9RkBAQF+bVlBQUADAzdcPN1+7hLwsy7TU11GRm43FZESWZdRaHSEJSbQ1NVKRk3WMjDfYrFbUajUyILRfjyAgy3ZH0vChIxEHKFJBQeF4eHp64unpyfDhw0+5rYubmilLTzkrDJC+0msjJCUlhQkTJmA0GnFxcWHlypXEx8cDcO+99zJx4kSWLFnS4/ZMJlOnlMmNjY29HZKCgsLvgKOKrC6eXo4yY0szmTu3ojM4Ez6856GHFrOJjB1biBw5Bp3B+eQXKCicZbTU15H80/dMurx/BcdON702QmJjYzlw4AANDQ18/fXXXH/99WzatIns7GzWr19PcnJyr9p79tlneeKJJ3o7DAUFBQX0zi6dZOJ7ikarI27SNI5s3ciQydPPO58ThfMXWZYpTkvBZrMx5cquGaLPNU7ZJ2T27NlERUXh5OTEK6+80ikrn81mQxRFpkyZwsaNG7u9vruVkJCQEMUnREFBYcAxtbZQfOQwUaPGnumhKCicFFmWSdu8nqjR49A7n32RVWckgZ0kSZhMJp544gluueWWTueSkpL4z3/+w+LFi497vU6nQ6c7O7JrKigo/L7QGZzxHBRIRW42/pGDz/RwFBROiqhS0VJXe1YaIX2hV0bIQw89xPz58wkNDaWpqYnPPvuMjRs3smbNGgICArp1Rg0NDSUiIqLfBqygoKDQn3gFBpObvAdzWytap7Mr2du5QMN339HWHnqqDQrC87rrlO2tAUIQBESVCksvMwmfzfTKCKmsrOS6666jrKwMd3d3hg4dypo1a5gzZ85AjU9BQUFhwIkYNoqc/XsYPHrcmR5KF9rMNr49UMKqQ2XMjPMjxt+VRqOFI2WNlNYbiQ1w4dYpkWdk4rfW1FD6wIOdyramryXxwSeJcItQjJEBQFSrsVmtZ3oY/UavjJB33323V42fZYrwCgoKCt0iiGInf7azBVmW2bt+Fw9trANga3b3+WjiB7kzOdrndA4NAPnoE7lKBTYbANEr95Pq9ArL21poil7MkzcuVoyR/kSSCYqLP9Oj6DfOvv91CgoKCqeZtqZGjM1NZ3oYDmRZpvGnn0gfEo/7vX/sdM7HRYevq46lwzsS1F3z7i6sNum3zQw40lEjxGbD/ZE7HOVDUqr4JG4n36ke4cnv/o257fx5cj/jCOfXA75ihCgoKPzuydm3myFTZpzpYTiQWlopufc+ADSyjcd3vuc4V99i4qmliTy8cAjLFnc8Ee8vrO/XMbQ0mKgqakKSjj/hSW0dvgkB0zu2slarO6Qaljd8wBe3fML+VVlYzLZ+HePvkZD4oRSnpZzpYfQb53eWKAUFBYUe4ObjR01xIVonJwzunqg1muPWTVm/FhdPLwzuHhjcPXD17v9tEEHsvH0xvjyNu5KX8+qwi7GKKm77eB9gXxU5Sml9W7/1L0kyXz2zh9YGMzqDmoQpQYQM8cQ/0h2VWkRsH59o6HDkFUPG4Pzic7T8/UGyAjvGv2iXhjH7nseY6sHPaXcweEJw+5sUGBQVg0fAoH4b9+8BvYsLlmNkLc51FCNEQUHhd09o4lDqyksxtbRQXVSALMl4+A/CMzCQqoJ8WuprAXv+mqaaKqqLCogeM4FWoYHynEwCBsfg6tV/xohoMBCXmkLtJ59Q+dzzAMwv2EV8bT6rIiaQPm4u2dWtVDd3TEZma/9txzRUttLaYAbA1Gpl/5oC9q+x5/DRu2gYd2EkcRMCEF061GYPP/824vv/BuCOVRLjcyYSUrYVvwb7VozeVE/jr++wem9HaKmzhyd/euvjfhv37wVTSzOyLJ8XvjaKEaKgoKAAeAbYfSx8w+ySAvXlZRQeOoBPWAT+EVGOehHDR3W6zj8iisLUg7TU1REQFX3SfsLDw7nnnnu45557HGXDhw9n6dKlLFu2zFEmqNW4zV9A667dNG/YAMC4+25j9pw5qH18aDFZeWdLHp/vLmR8pBcXjQzq61vvwsZPMxyvZ90whHUfHHEcG5stbPosg+RfCkkYbuBo1pKjBshRVLIXfg3210fz96h/47fSUl/Hjy+/wMK//A3hLHQMPhspSktBo3c6LwwQUIwQBQUFhW7xCBjU462C0MRh1JeXkbFjKyEJSRjc3PtlDBp/P0LeeL3bc846NXfPjubu2Sc3fHpLfWUrAAGRbsSNH0T0GH/qK1rR6tVk7ang4LoiGqva2PFLGxFhC3BrzEPrpMLU2kqLaxQ5kReCIFLU3IihrRJDawVOxhqGFlex7I5HEWSZ0Ye2M+zIXjK2b2bOrXcoOXx6SNbu7eeFXPtRFCNEQUFBoR/wCBiEu38ApZnplGam4xsahrvfuZlN/OhWTHluI5s+z2DalbF4B9q3UUbOCyN+ciA/vnqQirxGSuKXMPmRMdRarKQU1FOdVga7mwHIir7M0eb4nY8jCa3UefgCsHbaUtZOW0pi+j7uOI+iPQaagMhoNDr9mR5Gv6EYIQoKCgr9hCAIBMUOAaA8J4uqwgL0Bmf8IqPQ6s/NdOulWfWAPSzUYpPRqkX0zhou+utIio7U4h/uxqGSBva+dOKIjQq/EVj+7088UN/A89aOzyI1bhT3ZJfzUmIkepWyJXMiLGYTgkp1pofRryhGiIKCgsIAcNQ/xGxsoyI3G3NbG+6+foii2EXnwWKxnIkhHpc73pxJcXot3710AIvJxuqdRWR8nIXWBma1QGOwHsksoWux4t7Qs7Bbw9N3Mz3J7lszraiYO/OqyW2/9NvaZn7emsJnQ6OY6OmCTZb5Z04pxUYzt4f4Mcpd2aoBu59SwHmW40gxQhQUFBQGEK3eiZD4JAAq8nJwNTiRn5PtON/Y2EheXt5J2znd0RBH+2qqMdL0QRba9nKtVcYnv/tw4NpQJ8LH+ZO9owzJU8ugYFcCfQwMifQgdlBHVtWRIcFsDApkc10z1xzKBcAoyVx8IJsRrgaSm1odddfXNPLfIWEs8vMYkPd5LmE1mWioKMdzUP85IZ9pFCNEQUFB4TThHxHFgsUX8t677zJ2WBhRSTE8//yHqE6wxH7Lh3vJ37ONcfV7yDOEs8NzHIIocqyG2IHH5uBh0B63jb4gBjkhRThTV92GDOjNMi4mmaaRHvgNcsHDoMHJSY2btx5fZy0B3gZcdCq7/P2syJO2rxVFZnu7UTp9GP/MKeXNoiqATgYIQJskc8vhfIp8hqERz4+IkN4iSTZy9u3GNyScQdGxZ3o4/YpihCgoKCicRh566CHy8vK4/a9PodNKPLbs3m5XQpprazC1thJweBVDqvYA4NVwgFENB0hxjafQKYRcQwQIAiOe/IUJkd5Mj/Vleqwf0X4up7Rqsr6mkZtS8zCO1QE6QvRafhoVg4+2/6cMURC4P2IQo9ycabHZqLNYeSKnrEu9kE0H+WxoJDO93bpp5fzFarGQuXMrESNG4+TieqaH0+8I8lkmQt/Y2Ii7uzsNDQ24uf2+/tgUFBR+X1jMbWTu3I6zuycuXt74hIQBUJyWyldPPowsnVyAzCxo2OAzlUznaGg3PFx0ahYPG0R8oDuSJCOKArPj/LBVGGlsaOZQ/UHqPEsYFpiEQWPo1F61Ba7J7Dge5QKT3cBJlBEBUQBV+28RsJkseOGORlQhCgKiLNPZ/OmYYgRAlnGcP/a3IAqoRBFBENjZbOLVOiPIMlqrBbPGvsqjEwUKpg3r2Yd7npC+bRMx4ycjngMOqX2ZvxUjREFBQeEMI9lsNFbblVi9AoPY/e3XHN70KwBeQSHEjJ+M37Bx7EvJxma1kLV9E64V6V3aqdN4kGsIJ8Mlhhqtt6Pc3SZwDc4Ymjo7kW4PW8mhQRs51mqQBD01IW8PyPvsLaIkceO2VXwyfh6mdkOkfMbwMzuo08BR/5/qwnxsViv+54gzal/mb2U7RkFBQeEMI6pUePgH4OEfQFHaIYcBMv26Wxi1cKmjXnSsfTKSLl/I7v1p/PLlFxgKDzjOe1rqGdVwgOC2Er4KutRRPs2owWBpN0B0NjDZn6onFlzExIKLqHDJB6BRV0PasF9RGzdRoZ+GmyUDEJERsK+DCMiC2FFmETDYXJAEAVGlRhZAEjqvc3T3lCsL3a+VHFvXJkNUVQkayUaQ1Ui5Xs/T0eePQ2Z3VBXmU1dajFpnzwnk5uN3zhggfUUxQhQUFBTOIgpTO/Q2Nn70DkVpqUy9+ka8AjsmYFEUGT86kfGjn0KSJN7+96vU1tajbypHqizE31zlqOtjE4i1HLOUb+q6rO/fHO74fVHEdKaNGdN+ZvgJx/rhNx+Sd2hPp7K5V8xFFEVGRIxAp9Ed58qe8dJLL1EPfDU0kuDg4FNq61zAOziEmuJCIkeMOXnl8wRFGUZBQUHhLGLEBYtInDHXfiAI5OzdyQd//TPbl3+KJHXV5BBFkdv+dhcPPfMYiUnxjnId9sRxrt69U9dM21+IzdazZHiJcYldytZ+sZafP/uZf7z2j1712x1iez4Zm61nWiTnOqKowsnFDetZphszkCgrIQoKCgpnEQY3d+b96S7m/ekuaoqL2Pzpe+Tu38OOrz+nqiCfhXffj1qj6XKdzWqhJCPNcbzrgamYVE74uer45b00ijPqGDo9iCGTArEYbRzaWMzhzSVIts4bJlKyJ2/esZHGkCI0bgL33n71cUOIR8WPIu7hOLalb2P7N9sBMKlM6Gw6hOZTD6c92q/UAwfd84G2pkaMLc3dfr/nK4pjqoKCgsJZTtqWDax582Ukq5WIEaO58L6HUWs7dEGK0lJY9+4b1BQXArD4voeIGTfppO0WHq7hpzdTsFpOPMn/+fXpjlWJk5Gam8rXH32NUWXkuUefsxfKMq0laVh17rj59nxb5Y033qCiooJrrrmGwYPPb98IgMxd23Dz8cM3LAKV+txbI+jL/K1sxygoKCic5cRPmcHFDyxDpdGSl7yXb198EovJSGtDPT+99m++euIhaooLcXJ1Y+Hd9/fIAAEITfDm4r+PInKE7wnrpeZm9XisOq3dD0Rv01NcW2wvfMIDwzsTcXstgZL/LkQyt56ghQ5+byshMeMm4ebjy+GNv9LW1Himh3NaOPdMLQUFBYXfIWFDh3PJw0/wzbPLKDiUzCvXdUS/IAgMnTWPyVde32tBK99QV+bflkTq5hIOriuivqKzgdDkVUlMaM+MGgB3g555bMKLOrSvfATUdDofVLMV4zPh1C56j8DRi07Y1tHVl9+LEQJgcPcAQOtkOHHF8wTFCFFQUFA4RwiJT+LSfzzFl8secAiZ+YZHMvvm2wmMiTulthOnBpE4NYhtJdu4Y+2duLf5UWso48B1yajEngtl5f5wExPY3+25SnUwftZi9JgY9OPVFGweg8v8ZXgPmdxt/d+jEQLgHRKGxWhE5eJypocy4ChGiIKCgsI5RFDsEBb85W+sevkFIkeNZdHd96PR9S4C5njUGeu4d+O92EQrklcrdyfc1SsDBCCycF+n4wp9OJah1xI07x78VGpacnfR8N1DBDbsI6xxD3y5kEpNMGadN1bnQeARgs4/GreIkaiwR8X83owQi7ENjf7UwpvPFRTHVAUFBYVzFJvVSuaOLcRNmobQQ8fRE5Fcmcx1P10HwLNTniW3PhdJlhAFkT8O/SM6le6kOWnWvjeVuYUHu5SnL3iGuLF3OI5r0zbS/NMTBDclI3YraQYWVOQTgnnhqySMmXIK7+zcInvPTgaPGX+mh9FrFNl2BQUFhd8ZZmMbhamHGDx63Cm3tb1kO7f9ettxz3vpvbigOZIxy1MJym8GoD7QDY/SRmpuW8qEu58CycaBVbcTd/AbnI/RNdkdN4uxV3zTpc26vIMYM9ZjaqpBri9CbCpB11qGh7USPWYAqqY8g++sO7pcez5SkZeDRqfvJE53rqDItisoKCj8ztDqnXD18qaxqhI3X79TaquoqajTcbx3PGk1HdojIYerWfR1Jepjdkc8Su1RHN5vfUvd9o+QE4yMahdKO8oG7yDGX/Jxt316RgyDiK5J6SzFyfDOdAB8vD378nbOSWpLiwlLGn6mh3HaUIwQBQUFhXMc/8jBZO3ZcWpGiM3CU7ueIqA1gEkV7dEwefD8Dc9TqLbrj/g+a18lqQvzIicklG8TZhJTmMc1a75F72zBJ6HZ0VyKux91oWOJGn83M4LG9no4mu0vOV4LLv59f1/nGLHjJ5OxfTOxk6Yi9tIf51xEMUIUFBQUzgM8/AKoKyvBc1Afl/HXP0miyQRGn07FH3zwAcuWLcPW1MR+JwOL//0uoiQhtfugbBs+Buexo/hzyQvQXAlAK1Ek3dt9hEyPsZrsv+OXQtTMU2vrHEJUqQgbNpK0TetRaTR4B4eiMzhTW1IEgoBaoyFoSMJ5Y6AoYmUKCgoK5wG+YRFUtyum9gmVjnfKKilwKehUPHv2bKrMFh597FkW//tdAIcBcpRPvH3xbk53HKttnbd1TomomXASZ9jzDYObO4kz5jBk8nT0Li5YzWbCho0gYvgoAqJiyN6z80wPsd9QjBAFBQWF8wRnd8++K23OfATnG1azIuEPeA+xEZUUxZVXXsmYiRNJ2naY9y683FH1b2217Aj34iNnu3NIvcaNxyNvd5zXqswcTN9+Su9FwY6bjx/ewSGOlQ+NXo+LpxclGUewWa0nufrsR9mOUVBQUDhPCIiKpjD1IOHDRvatgbCJDAqbyF+OKbo/o2NVIzY/hxfHJTE2fjgAERGhFEsSDeUVZKkWUzPjGqpX3Eps/WGGfTGfzUl/Zuolz/X9DSl0y6DoOPIP7qe5thp3v4AzPZxTQjFCFBQUFM4TRJUKSerftPcflXbIrr+1YAruTk4A/Li/hF1rCggoMAJQHe5E7PDBBN7yI2WvTWRQWxlTU95gj2zGd8QfCI869RBiBTuCIGBua8PFy/tMD+WUUYwQBQUFhfOJAVR+mp5WSnyhiUt2tABw7DO4T34b73+Zxn23jkC8axc7P7mFYaW/MCL1XXIyP+Inr1jCJv4ZtXswezJWEuM7lDEjbu5Br2eVlNVZw+Ax4yk4lEzkyDFneiinhGKEKCgoKJxH9Kf+ZPLeQwTWNlLqZReeMhglhwFyFNWcQdh+KQNAt6+O/xlS0Y+r4xUPMGlDjqnZCMnPO45cS9axKf4KEAUERFQqbWfV19+ZM2pvkWxWNPr+kes/kyhGiIKCgsJ5Qt6BfTh79J+w1+YP8vmDypWdcW14N9gYlm/udH7yZdHohrizZl0ZPhEbcQ3ZS3Grnhe3ZZ207SZRYOQXHdLkg2wyXy79Dk+vqH4b//mKLMvkH9xPUGz8mR7KKaMYIQoKCgrnCRqtDrVW2y9tbVuRjdrqgotVZvbBtm7rlBdmQOM7JF66x1H2RlFHCvqbRjzOx4f/h6h2480Z/8dIjwAu+Wwy2bKxS1tlKoHsgg2MUYyQE1JXXkp1YT6hicPQGZzP9HBOGSVEV0FBQeE8ITg+kYbKii7l4eHhCIJAUlKSo2z48OEIgkB4eHi3bR345cSaI86+JajDbkPtvqdT+UivSMfrwWIx+69cy97Lvma0TxiiWseHl6zij95j+IvHcDYtXsnWpavws9lDfZ107j19q79bqgvyiR478bwwQEAxQhQUFBTOK1ob6o977vDhw7z00kudygoKCli2bFmXurNv7LzUHxTjQUi8l+M4ZEbHNT4uDzIyaRuzZubw4eLvWDgoFoBPjnzImvQ3+PLgc+wr+glJknBz9uMvi97jj0s+xstrMO7uoZjb/T+Eoyqp4FBf/b1hMRm79etpqa8jc9c2BNX5oZR6FGU7RkFBQeE8or/CNmPHBRAxzAdBFFBrRIR2Q8FitvHVS//sqNg8n2Ezb+107dIhf2ZV2T2ktVr5267X20s/xV/7MBeFz+COCf/uVP/oZs9L+1/m7aHX2A9qsu2/LV23bs4nJMmGubUNc1srbU2N1JWXonMyIAgCMmCzWhFFEZVGQ8y4SWd6uP2OYoQoKCgonEcIgoDNakWlViPLssN46AtafdcpQqNV4R6xzXE8bcG/u9QZFzyTGb6hbKkqwFurwUWlpsjYRoXZypuZvzAmaBVjQxc66g8XDeyiDeux4bhugWD8//buPSaqMw0D+DPDMMPcbXVARRahaCs2Wy+JBVvbgiCbJdbEqgS3cQjB1tZurxtirVmCicm2YdO60f5R22KUiEJrIl6y2Cy0GmVNMeIqSquCpYuLrbowg9zm8u0fCMvIHc7MmRmeXzIBzhzOvN/DDOedM+ebaQEME/tkYG8Rbje6OzvR1X4f3R3tDy4dY56dpFAooNbqoNHpoZ/yCCJi47xUsX9iE0JEFERmzHkC//y6GDPmPAEoAJfDAaVy4CvvDodj3Ldhmt4ChwOImf0nqFQDT4RVKBT42++PeyzbW70Vf609CgDodrV5XLdhzhqcu7Yf1YouHD2Vh99Gp6Cx/d9o12mxwjgDUk/WdTmd6Gq/33O533Nxu119t9PbRiiG+R4KBULDtNDodFBrdTBOnQZ1mNZzmjGNiE0IEVEQ0eh0WLr2D307Q0d3F4y6nnc5FUJACAGbzYb6+vpxbf/6jQI4HP8FAEyf/rtR/96cqQsA9DQhU8Ii0NLZgjZHG+477qOz38msWxsOAw2HAcsUAEBJ5y+Y99C2BmsixBjeKVYREoIwvQEanR7m8AiotTqEqLg7lANTJyIKMv2fjYeqNXg2IQH/qr0CANizZw++/PJLdHaO7VyL1tYa/Kf5azQ1HehbplZb0Nl5C06nHQ6nDU6nDU6nveer4/8/N7bU4i/1N9A7F2L93/8ItVMJtePBxanETDH4G2+1/qrEzY7znuNjExE0+FcjIgpyf96+HUWHSmBra8PVq1fH9LtCuHHp8mb8+utJz+Vu4B/li+DsCoGrSwlXd79ZGw+9hnHHqYC6RYfIB1cJBdAd6oYiTI1QYxjCdHroNAYYQg3Qh+phUPd8jZ8aj4S4lRMZOvk5NiFEREEuYmYkTpQWIyNnE5qamgAAOl0YHA4X1Go1OtvacLv+Ojrb2iDcLo8ewi0Eblw8Ayj0Hs2FQiEQonFDFaaAdpoeYQYjQlVGqFQmqEJNUPV+/+Dr/HYbbDAibtoSWPQzoA/VQ6XkLmiy4z2AiGgSiJr1G5w+dgS/dDtwo70TbpcLz057BBqdHhq9AWEGAyzRMVAO8j4Uj8zeg4abuxA1ywq1eqpHc6FUakY1AydqxDVoMlIIKT/tSAI2mw1msxmtra0w3y1pkgAACKFJREFUmUxyl0NERESjMJ79N+cSERERkSzYhBAREZEs2IQQERGRLNiEEBERkSzYhBAREZEs2IQQERGRLNiEEBERkSzYhBAREZEs2IQQERGRLNiEEBERkSzYhBAREZEs2IQQERGRLNiEEBERkSzYhBAREZEsVHIX8DAhBICejwQmIiKiwNC73+7dj4+G3zUhdrsdABAVFSVzJURERDRWdrsdZrN5VOsqxFhaFh9wu924desWjEYjFAqF3OUMymazISoqCj///DNMJpPc5QQd5utdzNd7mK13MV/vmmi+QgjY7XbMnDkTSuXozvbwuyMhSqUSs2bNkruMUTGZTHwgeBHz9S7m6z3M1ruYr3dNJN/RHgHpxRNTiYiISBZsQoiIiEgWbELGQaPRIC8vDxqNRu5SghLz9S7m6z3M1ruYr3fJka/fnZhKREREkwOPhBAREZEs2IQQERGRLNiEEBERkSzYhBAREZEs2ISMYMeOHVi6dCl0Oh2mTJky4PqLFy8iMzMTUVFR0Gq1mDdvHnbu3Dnk9s6cOQOVSoUFCxZ4r+gAIUW2hw8fRmpqKiwWC0wmExITE1FeXu6jEfg3qe673377LRYtWgSNRoO4uDjs3bvX+8UHgJHyBYA333wTixcvhkajGfIxX15ejoSEBBiNRlgsFrz00ku4efOm1+oOBFJlK4RAQUEB5s6dC41Gg8jISOzYscN7hQcIqfLtdf36dRiNxiG3NRw2ISPo7u7G2rVr8dprrw16/fnz5xEeHo6ioiLU1tbigw8+wPvvv49du3YNWLelpQUbNmzA8uXLvV12QJAi21OnTiE1NRUnTpzA+fPnkZSUhJUrV+LChQu+GobfkiLfhoYGpKenIykpCTU1NXj77beRk5PDRg8j59srOzsbGRkZg17X0NCAVatWITk5GTU1NSgvL8edO3ewevVqb5QcMKTIFgDeeustfP755ygoKEBdXR3KysqwZMkSqcsNOFLlCwAOhwOZmZlYtmzZ+IoRNCqFhYXCbDaPat3XX39dJCUlDViekZEhtm3bJvLy8sRTTz0lbYEBTIps+4uPjxf5+fkSVBYcJpJvbm6umD9/vsc6GRkZIi0tTcoSA9po8h3qMV9aWipUKpVwuVx9y8rKyoRCoRDd3d0SVxp4JpLtlStXhEqlEnV1dd4pLghMJN9eubm54uWXXx7T/5n+eCTEC1pbW/Hoo496LCssLER9fT3y8vJkqio4DJZtf263G3a7fdh1aGgP51tVVYWUlBSPddLS0lBVVeXr0oLS4sWLoVQqUVhYCJfLhdbWVuzfvx8pKSkIDQ2Vu7yAdvToUcTGxuLYsWOIiYnB7NmzkZOTg3v37sldWtCoqKhAaWkpdu/ePe5t+N0H2AW6s2fP4tChQzh+/HjfsmvXrmHLli04ffo0VCpGPl6DZfuwgoICtLW1Yd26dT6sLDgMlm9zczMiIiI81ouIiIDNZkNHRwe0Wq2vywwqMTExOHnyJNatW4dXX30VLpcLiYmJOHHihNylBbz6+nr89NNPKC0txb59++ByufDOO+9gzZo1qKiokLu8gHf37l1kZWWhqKhoQh8mOCmPhGzZsgUKhWLYS11d3Zi3e/nyZaxatQp5eXlYsWIFAMDlcmH9+vXIz8/H3LlzpR6K3/Fltg87cOAA8vPzUVJSgvDw8IkOxS/Jme9k4K18h9Lc3IyNGzfCarXi+++/x3fffQe1Wo01a9ZABNmbWfs6W7fbja6uLuzbtw/Lli3DCy+8gC+++AKVlZX44YcfJLsdf+HrfDdu3Ij169fjueeem9B2JuXT8vfeew9ZWVnDrhMbGzumbV65cgXLly/HK6+8gm3btvUtt9vtqK6uxoULF/DGG28A6HlwCCGgUqlw8uRJJCcnj3kM/sqX2fZ38OBB5OTkoLS0dMDLB8HE1/lOnz4dt2/f9lh2+/ZtmEymoDwK4o18h7N7926YzWZ89NFHfcuKiooQFRWFc+fOISEhQbLbkpuvs50xYwZUKpXHk7958+YBABobG/H4449Ldlv+wNf5VlRUoKysDAUFBQB6ZiK53W6oVCp89tlnyM7OHtV2JmUTYrFYYLFYJNtebW0tkpOTYbVaB0z/MplMuHTpkseyTz/9FBUVFfjqq68QExMjWR3+wJfZ9iouLkZ2djYOHjyI9PR0yW7bH/k638FeGvjmm2+QmJgoWQ3+ROp8R9Le3g6l0vOAdEhICICeJyvBxNfZPvPMM3A6nbhx4wYee+wxAMCPP/4IAIiOjvZZHb7i63yrqqrgcrn6fj5y5Ag+/PBDnD17FpGRkaPezqRsQsaisbER9+7dQ2NjI1wuF2pqagAAcXFxMBgMuHz5MpKTk5GWloZ3330Xzc3NAHr+kVgsFiiVSjz55JMe2wwPD0dYWNiA5ZPNRLMFel6CsVqt2LlzJ55++um+dbRaLcxmsyzj8hdS5Ltp0ybs2rULubm5yM7ORkVFBUpKSoY9L2eyGClfoOf9E9ra2tDc3IyOjo6+deLj46FWq5Geno6PP/4Y27dvR2ZmJux2O7Zu3Yro6GgsXLhQppHJT4psU1JSsGjRImRnZ+OTTz6B2+3G5s2bkZqaOileGh+OFPn2HlXqVV1dPej+bkRjnk8zyVitVgFgwKWyslII0TN9abDro6Ojh9wmp+j2kCLb559/ftB1rFarLGPyJ1LddysrK8WCBQuEWq0WsbGxorCw0Odj8Ucj5SvE0PfPhoaGvnWKi4vFwoULhV6vFxaLRbz44ovi6tWrvh+QH5Eq26amJrF69WphMBhERESEyMrKEnfv3vX9gPyMVPn2N94pugohguzsJyIiIgoIk3J2DBEREcmPTQgRERHJgk0IERERyYJNCBEREcmCTQgRERHJgk0IERERyYJNCBEREcmCTQgRERHJgk0IERERyYJNCBEREcmCTQgRERHJgk0IERERyeJ/hxIiAW9JuWoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t]/aDP for t in popHDlist],bins = [0.4, 0.6, 0.7, 0.85, 0.9, 0.95,\n",
    "         0.98, 1.0, 1.02, 1.05, 1.1, 1.15, 1.3, 1.5, 2.0])\n",
    "plt.axvline(1.0,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "9bc31b5a-992d-4a38-b9fe-3470b55e1e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigButOffPop.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6150cac-2249-415b-a364-edf55dc441ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00199 0.1255\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 801\n",
      "working on avg unit-to-HDcp dist for HD 1603\n",
      "working on avg unit-to-HDcp dist for HD 2405\n",
      "working on avg unit-to-HDcp dist for HD 3208\n",
      "working on avg unit-to-HDcp dist for HD 4008\n",
      "working on avg unit-to-HDcp dist for HD 4808\n",
      "working on avg unit-to-HDcp dist for HD 5609\n",
      "working on avg unit-to-HDcp dist for HD 6409\n",
      "working on avg unit-to-HDcp dist for HD 7212\n",
      "working on avg unit-to-HDcp dist for HD 8016\n",
      "working on avg unit-to-HDcp dist for HD 8816\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "f5b99900-614a-47df-b7de-5d59fffd6d3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00199 0.1255\n"
     ]
    }
   ],
   "source": [
    "#Restart - may want to comment out first line\n",
    "#HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()\n",
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cea58fc2-c389-474a-9800-0f4906640a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "#WA - OPTIONAL restart here, pull in file*********************\n",
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for squaring up\") \n",
    "print(\"Must have already established unitGeoms, pops, topology in above blocks\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigButOffPopUnit.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse,weights = unitPop)\n",
    "print(\"weighted unit use histogram\")\n",
    "plt.show()\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 627 HDs with pop < 752746 to within 3791\n",
      "working on squaring up HD 17 time is now 0\n",
      "working on squaring up HD 1356 time is now 28\n",
      "working on squaring up HD 3273 time is now 62\n",
      "working on squaring up HD 4811 time is now 88\n",
      "working on squaring up HD 5914 time is now 152\n",
      "working on squaring up HD 7351 time is now 202\n",
      "working on squaring up HD 8894 time is now 226\n",
      "We have addressed underpop in a total of 627 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) ) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)  #NOTE - HAVE NOT MOVED TO AVOIDEDENCLAVE HERE (YET)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amped unit avg and sd usage are 1.00499 0.12494\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#prevUnitUse = [0.]*nUnits\n",
    "#for t in popHDlist:\n",
    "#    for u in latestHDlist[t]:\n",
    "#        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "#print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "ee512b2b-4daa-4a6b-9452-e8dfdfdebc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the current barredJettisonSet (usually CCB's) is {9020, 9021, 9022, 9023}\n",
      "9020 1.0082877017512935\n",
      "9021 0.8522727994123822\n",
      "9022 0.954693993202511\n",
      "9023 1.0666279058621464\n"
     ]
    }
   ],
   "source": [
    "#CHECK ON BARRED JETTISON SET BEFORE RUNNING BELOW\n",
    "barredJettisonSet = set()\n",
    "for u in range(nUnits):\n",
    "    if abs(allUnits[u] - int(allUnits[u]) - 0.25) < 0.01:\n",
    "        barredJettisonSet.add(u)\n",
    "print(\"the current barredJettisonSet (usually CCB's) is\",barredJettisonSet)\n",
    "for u in barredJettisonSet:\n",
    "    print(u,unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "5a6cb84c-1d0f-4f02-831c-985fe4078dc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{9020, 9021, 9022} {9020, 9021, 9022, 9023}\n"
     ]
    }
   ],
   "source": [
    "origBarredJettisonSet = set(list(barredJettisonSet))\n",
    "for u in origBarredJettisonSet:\n",
    "    if unitUse[u] > 1.04:\n",
    "        barredJettisonSet.remove(u)  #this one is overused in this state\n",
    "print(barredJettisonSet, origBarredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "477692b7-5cdd-48bb-b4eb-3eb8755534e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDunitList = [ampedHDlist[t] for t in range(nHDs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 15 overPopped HDs to within 3791\n",
      "working on squaring down HD 910 time is now 0\n",
      "With short check, appear to have enclave; need to do long check for HD 910\n",
      "With short check, appear to have enclave; need to do long check for HD 1116\n",
      "With short check, appear to have enclave; need to do long check for HD 1741\n",
      "With short check, appear to have enclave; need to do long check for HD 2156\n",
      "With short check, appear to have enclave; need to do long check for HD 2245\n",
      "With short check, appear to have enclave; need to do long check for HD 2250\n",
      "With short check, appear to have enclave; need to do long check for HD 4393\n",
      "With short check, appear to have enclave; need to do long check for HD 5071\n",
      "With short check, appear to have enclave; need to do long check for HD 5226\n",
      "With short check, appear to have enclave; need to do long check for HD 5463\n",
      "With short check, appear to have enclave; need to do long check for HD 8638\n",
      "With short check, appear to have enclave; need to do long check for HD 8997\n",
      "With short check, appear to have enclave; need to do long check for HD 9006\n",
      "With short check, appear to have enclave; need to do long check for HD 9012\n",
      "With short check, appear to have enclave; need to do long check for HD 9014\n",
      "We have addressed overpop in a total of 15 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN  #the barred set is dynamic; we stop shedding upon underuse\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))  \n",
    "barredSet = set(list(barredJettisonSet))\n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    for u in origBarredJettisonSet:\n",
    "        if unitUse[u] < 1.00:\n",
    "            barredSet = barredSet.union({u})  #adding back in when become underused \n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "            print(\"With short check, appear to have enclave; need to do long check for HD\",t)  #new 2 lines to help CA SF Bay Area\n",
    "            noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not (unbroken and noEnclave):      \n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        #barredSet = barredJettisonSet  #see above for VA-on modification\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                contig = avoidedIsland(tryList, unitNbrs, [unitNoToShed] )  #new for MO - faster contig check\n",
    "                #unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                #if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                if contig:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c4c929d-0208-4785-aa15-5681a493a530",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "amped unit avg and sd usage are 1.00499 0.12494\n",
      "shed unit avg and sd usage are 1.00226 0.12008\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 9129\n",
      "working on HD 601 out of 9129\n",
      "working on HD 1201 out of 9129\n",
      "working on HD 1803 out of 9129\n",
      "working on HD 2405 out of 9129\n",
      "working on HD 3008 out of 9129\n",
      "working on HD 3608 out of 9129\n",
      "working on HD 4208 out of 9129\n",
      "working on HD 4808 out of 9129\n",
      "working on HD 5409 out of 9129\n",
      "working on HD 6009 out of 9129\n",
      "working on HD 6610 out of 9129\n",
      "working on HD 7212 out of 9129\n",
      "working on HD 7816 out of 9129\n",
      "working on HD 8416 out of 9129\n",
      "working on HD 9018 out of 9129\n",
      "all done checking HD and complement contiguity for all 9129 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 627\n",
      "overpop contigFail, enclaveFail = 0 0 out of 15\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 9020 with pop 179702.0 now has use 1.00829\n",
      "CCB unit 9021 with pop 27743.0 now has use 0.85227\n",
      "CCB unit 9022 with pop 44076.0 now has use 0.95469\n",
      "CCB unit 9023 with pop 41430.0 now has use 1.06663\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "#latestAvg, latestSD = getWeightedAvgAndSD(latestUnitUse,unitPop)\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "#print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%600 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "f6e52cd9-04dd-4000-8b9d-77170924b6cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47\n"
     ]
    }
   ],
   "source": [
    "couldAddUnderU = list()  #CA special: shoring up underused unit before we patch\n",
    "underU = 9021\n",
    "for t in popHDlist:\n",
    "    if underU not in HDunitList[t]:\n",
    "        if len(set(HDunitList[t]).intersection(set(unitNbrs[underU])) ) > 0:           \n",
    "            complementContig = avoidedEnclave(HDunitList[t], unitNbrs, [underU])\n",
    "            if complementContig:\n",
    "                couldAddUnderU.append(t)\n",
    "print(len(couldAddUnderU))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "51f7af0f-8e71-43f9-be25-9321eaa74f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "added unit 9021 to HD 4043 underUse now 0.85864\n",
      "added unit 9021 to HD 5771 underUse now 0.86517\n",
      "added unit 9021 to HD 4557 underUse now 0.86939\n",
      "added unit 9021 to HD 7597 underUse now 0.87742\n",
      "added unit 9021 to HD 2422 underUse now 0.88542\n",
      "added unit 9021 to HD 8303 underUse now 0.89197\n",
      "added unit 9021 to HD 2454 underUse now 0.9003\n",
      "added unit 9021 to HD 5773 underUse now 0.90221\n",
      "added unit 9021 to HD 8400 underUse now 0.90763\n",
      "added unit 9021 to HD 2458 underUse now 0.91689\n",
      "added unit 9021 to HD 5712 underUse now 0.91914\n",
      "added unit 9021 to HD 8652 underUse now 0.92204\n",
      "added unit 9021 to HD 4953 underUse now 0.92674\n",
      "added unit 9021 to HD 7309 underUse now 0.93103\n",
      "added unit 9021 to HD 7303 underUse now 0.93332\n",
      "added unit 9021 to HD 5056 underUse now 0.93453\n",
      "added unit 9021 to HD 5068 underUse now 0.93542\n",
      "added unit 9021 to HD 4961 underUse now 0.94011\n",
      "added unit 9021 to HD 4611 underUse now 0.94458\n",
      "added unit 9021 to HD 5711 underUse now 0.94645\n",
      "added unit 9021 to HD 7431 underUse now 0.94968\n",
      "added unit 9021 to HD 3818 underUse now 0.95178\n",
      "added unit 9021 to HD 3816 underUse now 0.95476\n",
      "added unit 9021 to HD 7311 underUse now 0.95999\n",
      "added unit 9021 to HD 2728 underUse now 0.9687\n",
      "added unit 9021 to HD 5710 underUse now 0.97337\n",
      "added unit 9021 to HD 8658 underUse now 0.98163\n",
      "added unit 9021 to HD 4063 underUse now 0.985\n",
      "added unit 9021 to HD 8680 underUse now 0.99024\n"
     ]
    }
   ],
   "source": [
    "#CA special\n",
    "couldAddDist = [hdCP[t].distance(unitCP[underU]) for t in couldAddUnderU] #add to HDs close to underU until full\n",
    "idxNo = 0\n",
    "idx = np.argsort(couldAddDist)\n",
    "while unitUse[underU] < 0.99 and idxNo < len(couldAddDist) :\n",
    "    t = couldAddUnderU[idx[idxNo]]\n",
    "    HDunitList[t].append(underU)\n",
    "    HDvPop[t] += unitPop[underU]\n",
    "    unitUse[underU] += nDistricts * HDweight[t]\n",
    "    print( \"added unit\",underU,\"to HD\",t,\"underUse now\",r5(unitUse[underU]) )\n",
    "    idxNo +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "bb4a4740-afda-4d7a-b960-72df6faf5378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now square down again -- should just be the ones we added on the underused unit\n",
      "Let's now square down the 29 overPopped HDs to within 3791\n",
      "working on squaring down HD 2422 time is now 0\n",
      "We have addressed overpop in a total of 29 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CA special -- square these down\n",
    "#SQUARING DOWN  #the barred set is dynamic; we stop shedding upon underuse\n",
    "print(\"Now square down again -- should just be the ones we added on the underused unit\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))  \n",
    "barredSet = set(list(barredJettisonSet))\n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    for u in origBarredJettisonSet:\n",
    "        if unitUse[u] < 1.00:\n",
    "            barredSet = barredSet.union({u})  #adding back in when become underused \n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "            print(\"With short check, appear to have enclave; need to do long check for HD\",t)  #new 2 lines to help CA SF Bay Area\n",
    "            noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not (unbroken and noEnclave):      \n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        #barredSet = barredJettisonSet  #see above for VA-on modification\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                contig = avoidedIsland(tryList, unitNbrs, [unitNoToShed] )  #new for MO - faster contig check\n",
    "                #unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                #if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                if contig:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "357c246d-3996-4fcb-89bc-dcd52cd46663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "amped unit avg and sd usage are 1.00499 0.12494\n",
      "shed unit avg and sd usage are 1.00226 0.11998\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 9129\n",
      "working on HD 601 out of 9129\n",
      "working on HD 1201 out of 9129\n",
      "working on HD 1803 out of 9129\n",
      "working on HD 2405 out of 9129\n",
      "working on HD 3008 out of 9129\n",
      "working on HD 3608 out of 9129\n",
      "working on HD 4208 out of 9129\n",
      "working on HD 4808 out of 9129\n",
      "working on HD 5409 out of 9129\n",
      "working on HD 6009 out of 9129\n",
      "working on HD 6610 out of 9129\n",
      "working on HD 7212 out of 9129\n",
      "working on HD 7816 out of 9129\n",
      "working on HD 8416 out of 9129\n",
      "working on HD 9018 out of 9129\n",
      "all done checking HD and complement contiguity for all 9129 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 627\n",
      "overpop contigFail, enclaveFail = 0 0 out of 29\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 9020 with pop 179702.0 now has use 1.00829\n",
      "CCB unit 9021 with pop 27743.0 now has use 0.99024\n",
      "CCB unit 9022 with pop 44076.0 now has use 0.95469\n",
      "CCB unit 9023 with pop 41430.0 now has use 1.06663\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "#latestAvg, latestSD = getWeightedAvgAndSD(latestUnitUse,unitPop)\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "#print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%600 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "ff3288f1-f9ca-473c-886c-ee8d6c2637bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9129 9024\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, nUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ed668f8-bf1e-46eb-963f-cf5e2b935b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see MD code if we need to remove a stuck CCB's from any HDs and then redo the square-up after dropping them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigGoodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "7a36c0ba-5a68-445b-a223-b7fa55dfd728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 9129 HD centers\n"
     ]
    }
   ],
   "source": [
    "nHDs = len(vtdGeom)\n",
    "print(\"there are\",nHDs,\"HD centers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology -- or read in via next block\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./2024state_HD_output/CA9129opt3ContigGoodPop.csv ./2024state_HD_output/CA9129opt3ContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of HDweight should be unity, actually is 0.999999999999818\n",
      "I will now renormalize\n",
      "normalized weight is now 1.0\n",
      "I read in 9129 HD lists of units\n",
      "orig unit avg and sd usage are 1.00226 0.11998\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology -- or read in via next block\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigGoodPop.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8014f0bf-74bb-40bc-b217-af4e204ea9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upon restart, need to pull in unit topology and data\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv ./state_map_files/MNunitTopologies_06Apr24.csv\n"
     ]
    }
   ],
   "source": [
    "#SKIP THE BELOW IF NOT A COLD RESTART\n",
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "\n",
    "nbrListString = topDF[\"neighborList\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "unitParentVTDno = topDF[\"unitParentVTDno\"]  #NOTE: some units are VTD fragments, so they share a parentVTDno\n",
    "uVLstring = topDF[\"unitVTDlist\"]\n",
    "unitNbrs = [ast.literal_eval(uVLstring[u]) for u in range(nUnits)]\n",
    "allUnits = topDF[\"allUnits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working avg precinct-to-HDcP distance for HD 0\n",
      "working avg precinct-to-HDcP distance for HD 800\n",
      "working avg precinct-to-HDcP distance for HD 1600\n",
      "working avg precinct-to-HDcP distance for HD 2400\n",
      "working avg precinct-to-HDcP distance for HD 3200\n",
      "working avg precinct-to-HDcP distance for HD 4000\n",
      "working avg precinct-to-HDcP distance for HD 4800\n",
      "working avg precinct-to-HDcP distance for HD 5600\n",
      "working avg precinct-to-HDcP distance for HD 6400\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking for discontiguities for HD 500\n",
      "checking for discontiguities for HD 1000\n",
      "checking for discontiguities for HD 1500\n",
      "checking for discontiguities for HD 2000\n",
      "checking for discontiguities for HD 2500\n",
      "checking for discontiguities for HD 3000\n",
      "checking for discontiguities for HD 3500\n",
      "checking for discontiguities for HD 4000\n",
      "checking for discontiguities for HD 4500\n",
      "checking for discontiguities for HD 5000\n",
      "checking for discontiguities for HD 5500\n",
      "checking for discontiguities for HD 6000\n",
      "checking for discontiguities for HD 6500\n",
      "checking for discontiguities for HD 7000\n",
      "out of 6678 total HDs, there were 6678 0 0 0 HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"checking for discontiguities for HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)   \n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "9020 1.00829 179702.0\n",
      "9021 0.99024 27743.0\n",
      "9022 0.95469 44076.0\n",
      "9023 1.06663 41430.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc95ca58-c27f-4ac9-b6f7-234a94945f07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa2a97c4-11f3-48dd-b1b6-f7e98246dd8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for IL, only use avoidedEnclave / Island, not conventional patchUp method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "fd36caf6-71e4-44a4-9d09-7469512c5e05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used.  Using new-for-WI avoidedEnclave routines\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.065\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 2166 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 200\n",
      "enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 3789 units out of 9024 with usage below 0.98\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 38017\n",
      "current avg and SD of unit usage are 1.00226 0.11998 . Now trying to increase up to 200 units' underusage\n",
      "all done trying to increase usage of unit 7263 final usage = 0.98234 43 sec elapsed 78 8 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00226 0.11943\n",
      "all done trying to increase usage of unit 4511 final usage = 0.98435 124 sec elapsed 69 3 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00226 0.11848\n",
      "all done trying to increase usage of unit 6267 final usage = 0.78686 185 sec elapsed 23 14 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00227 0.11808\n",
      "all done trying to increase usage of unit 6020 final usage = 0.7715 286 sec elapsed 25 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00227 0.11774\n",
      "all done trying to increase usage of unit 164 final usage = 0.7741 469 sec elapsed 22 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00227 0.11748\n",
      "all done trying to increase usage of unit 69 final usage = 0.81579 567 sec elapsed 26 26 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00228 0.11717\n",
      "all done trying to increase usage of unit 221 final usage = 0.86853 763 sec elapsed 38 12 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00228 0.11685\n",
      "all done trying to increase usage of unit 4506 final usage = 0.9852 860 sec elapsed 55 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.11639\n",
      "all done trying to increase usage of unit 3405 final usage = 0.96823 886 sec elapsed 58 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.11528\n",
      "begin usage increase for unit 86 with usage 0.69832 . Sec, total UU units tried = 891 10\n",
      "all done trying to increase usage of unit 86 final usage = 0.91184 1019 sec elapsed 41 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.11502\n",
      "all done trying to increase usage of unit 6022 final usage = 0.92391 1278 sec elapsed 45 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.11479\n",
      "all done trying to increase usage of unit 3414 final usage = 0.98302 1328 sec elapsed 55 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.11384\n",
      "all done trying to increase usage of unit 4861 final usage = 0.98273 1381 sec elapsed 46 6 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.11277\n",
      "all done trying to increase usage of unit 8453 final usage = 0.98452 1442 sec elapsed 64 2 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00229 0.11223\n",
      "all done trying to increase usage of unit 2856 final usage = 0.98019 1469 sec elapsed 52 1 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0023 0.11139\n",
      "all done trying to increase usage of unit 3415 final usage = 0.98408 1513 sec elapsed 49 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0023 0.1106\n",
      "all done trying to increase usage of unit 8916 final usage = 0.98139 1521 sec elapsed 42 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0023 0.10996\n",
      "all done trying to increase usage of unit 5339 final usage = 0.94333 1965 sec elapsed 36 141 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0023 0.10919\n",
      "all done trying to increase usage of unit 2221 final usage = 0.98479 1980 sec elapsed 30 22 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00229 0.10879\n",
      "begin usage increase for unit 6944 with usage 0.74617 . Sec, total UU units tried = 1985 20\n",
      "all done trying to increase usage of unit 6944 final usage = 0.98105 2015 sec elapsed 37 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.1077\n",
      "all done trying to increase usage of unit 138 final usage = 0.98188 2030 sec elapsed 43 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00229 0.10721\n",
      "all done trying to increase usage of unit 8690 final usage = 0.98246 2042 sec elapsed 39 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.10681\n",
      "all done trying to increase usage of unit 18 final usage = 0.98313 2081 sec elapsed 36 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.10604\n",
      "all done trying to increase usage of unit 7735 final usage = 0.98261 2122 sec elapsed 40 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00228 0.10508\n",
      "all done trying to increase usage of unit 6028 final usage = 0.9887 2339 sec elapsed 42 10 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.10471\n",
      "all done trying to increase usage of unit 1281 final usage = 0.98416 2348 sec elapsed 39 10 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00228 0.10441\n",
      "all done trying to increase usage of unit 8454 final usage = 0.93414 2442 sec elapsed 47 13 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00228 0.10413\n",
      "all done trying to increase usage of unit 8919 final usage = 0.98172 2458 sec elapsed 41 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00228 0.10375\n",
      "all done trying to increase usage of unit 6990 final usage = 0.98465 2511 sec elapsed 33 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.10298\n",
      "begin usage increase for unit 5003 with usage 0.77113 . Sec, total UU units tried = 2515 30\n",
      "all done trying to increase usage of unit 5003 final usage = 0.9886 2544 sec elapsed 33 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.10216\n",
      "all done trying to increase usage of unit 4503 final usage = 0.98015 2622 sec elapsed 44 3 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00228 0.10184\n",
      "all done trying to increase usage of unit 6015 final usage = 0.90016 2898 sec elapsed 21 48 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.00229 0.10159\n",
      "all done trying to increase usage of unit 3203 final usage = 0.98422 2955 sec elapsed 39 5 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00229 0.10083\n",
      "all done trying to increase usage of unit 2321 final usage = 0.98471 2966 sec elapsed 35 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.10064\n",
      "all done trying to increase usage of unit 4240 final usage = 0.98145 2983 sec elapsed 35 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.09996\n",
      "all done trying to increase usage of unit 6982 final usage = 0.98498 2997 sec elapsed 34 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.0993\n",
      "all done trying to increase usage of unit 3615 final usage = 0.98014 3473 sec elapsed 38 131 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.09872\n",
      "all done trying to increase usage of unit 6366 final usage = 0.98597 3493 sec elapsed 38 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.09806\n",
      "all done trying to increase usage of unit 3980 final usage = 0.9819 3541 sec elapsed 35 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.09734\n",
      "begin usage increase for unit 8620 with usage 0.78055 . Sec, total UU units tried = 3545 40\n",
      "all done trying to increase usage of unit 8620 final usage = 0.98395 3606 sec elapsed 35 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.09679\n",
      "all done trying to increase usage of unit 3401 final usage = 0.98217 3647 sec elapsed 47 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.09629\n",
      "all done trying to increase usage of unit 7715 final usage = 0.98233 3655 sec elapsed 36 5 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00227 0.0959\n",
      "all done trying to increase usage of unit 7129 final usage = 0.98874 3666 sec elapsed 27 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00226 0.09548\n",
      "all done trying to increase usage of unit 8362 final usage = 0.98194 3737 sec elapsed 32 4 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00227 0.09489\n",
      "all done trying to increase usage of unit 8599 final usage = 0.98495 3774 sec elapsed 32 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00227 0.09434\n",
      "all done trying to increase usage of unit 1558 final usage = 0.98535 3789 sec elapsed 32 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00226 0.09383\n",
      "all done trying to increase usage of unit 3555 final usage = 0.982 3826 sec elapsed 32 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00226 0.09331\n",
      "all done trying to increase usage of unit 3641 final usage = 0.98301 3900 sec elapsed 33 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00227 0.09281\n",
      "all done trying to increase usage of unit 8787 final usage = 0.89312 4111 sec elapsed 17 36 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00226 0.09264\n",
      "begin usage increase for unit 5783 with usage 0.79063 . Sec, total UU units tried = 4115 50\n",
      "all done trying to increase usage of unit 5783 final usage = 0.98428 4132 sec elapsed 33 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00226 0.09206\n",
      "all done trying to increase usage of unit 6971 final usage = 0.98297 4142 sec elapsed 30 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00225 0.0916\n",
      "all done trying to increase usage of unit 8428 final usage = 0.98111 4268 sec elapsed 42 5 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00225 0.09113\n",
      "all done trying to increase usage of unit 1277 final usage = 0.9807 4277 sec elapsed 28 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00225 0.09097\n",
      "all done trying to increase usage of unit 6635 final usage = 0.98152 4305 sec elapsed 36 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.09034\n",
      "all done trying to increase usage of unit 884 final usage = 0.98307 4366 sec elapsed 32 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.09011\n",
      "all done trying to increase usage of unit 6700 final usage = 0.9851 4611 sec elapsed 27 18 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.08968\n",
      "all done trying to increase usage of unit 3773 final usage = 0.98717 4729 sec elapsed 36 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.08923\n",
      "all done trying to increase usage of unit 6805 final usage = 0.98381 4779 sec elapsed 28 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.08882\n",
      "all done trying to increase usage of unit 2152 final usage = 0.98352 4807 sec elapsed 29 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.0885\n",
      "begin usage increase for unit 2207 with usage 0.80569 . Sec, total UU units tried = 4810 60\n",
      "all done trying to increase usage of unit 2207 final usage = 0.98076 4847 sec elapsed 28 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08806\n",
      "all done trying to increase usage of unit 4827 final usage = 0.98089 4860 sec elapsed 31 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08786\n",
      "all done trying to increase usage of unit 6947 final usage = 0.98534 4903 sec elapsed 31 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.08753\n",
      "all done trying to increase usage of unit 488 final usage = 0.98039 4924 sec elapsed 32 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.08714\n",
      "all done trying to increase usage of unit 4307 final usage = 0.98028 4931 sec elapsed 24 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08665\n",
      "all done trying to increase usage of unit 5397 final usage = 0.98499 4949 sec elapsed 27 14 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08639\n",
      "all done trying to increase usage of unit 5558 final usage = 0.984 4958 sec elapsed 31 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08594\n",
      "all done trying to increase usage of unit 6984 final usage = 0.98009 4982 sec elapsed 28 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08561\n",
      "all done trying to increase usage of unit 6693 final usage = 0.98424 5027 sec elapsed 27 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08529\n",
      "all done trying to increase usage of unit 4890 final usage = 0.98124 5106 sec elapsed 28 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00224 0.085\n",
      "begin usage increase for unit 165 with usage 0.81142 . Sec, total UU units tried = 5111 70\n",
      "all done trying to increase usage of unit 165 final usage = 0.98189 5219 sec elapsed 33 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00224 0.08486\n",
      "all done trying to increase usage of unit 2497 final usage = 0.98385 5248 sec elapsed 28 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00224 0.08456\n",
      "all done trying to increase usage of unit 1165 final usage = 0.98321 5358 sec elapsed 36 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08418\n",
      "all done trying to increase usage of unit 5141 final usage = 0.98276 5401 sec elapsed 26 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00224 0.08396\n",
      "all done trying to increase usage of unit 6449 final usage = 0.98002 5412 sec elapsed 28 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08382\n",
      "all done trying to increase usage of unit 2185 final usage = 0.98698 5419 sec elapsed 26 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08366\n",
      "all done trying to increase usage of unit 66 final usage = 0.94653 5593 sec elapsed 24 1 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00223 0.08352\n",
      "all done trying to increase usage of unit 6088 final usage = 0.98288 5602 sec elapsed 29 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.08281\n",
      "all done trying to increase usage of unit 4898 final usage = 0.98077 5655 sec elapsed 27 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.08254\n",
      "all done trying to increase usage of unit 238 final usage = 0.98214 5716 sec elapsed 28 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.08195\n",
      "begin usage increase for unit 7272 with usage 0.81416 . Sec, total UU units tried = 5720 80\n",
      "all done trying to increase usage of unit 7272 final usage = 0.98414 5740 sec elapsed 38 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08152\n",
      "all done trying to increase usage of unit 3974 final usage = 0.98371 5852 sec elapsed 27 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.08123\n",
      "all done trying to increase usage of unit 5287 final usage = 0.98181 5875 sec elapsed 31 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00224 0.08093\n",
      "all done trying to increase usage of unit 452 final usage = 0.98235 5898 sec elapsed 28 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.08066\n",
      "all done trying to increase usage of unit 6687 final usage = 0.98518 5946 sec elapsed 27 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00223 0.08035\n",
      "all done trying to increase usage of unit 2415 final usage = 0.98049 6001 sec elapsed 22 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.08016\n",
      "all done trying to increase usage of unit 4940 final usage = 0.98063 6016 sec elapsed 30 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07985\n",
      "all done trying to increase usage of unit 5929 final usage = 0.99222 6098 sec elapsed 28 13 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07952\n",
      "all done trying to increase usage of unit 2446 final usage = 0.98086 6150 sec elapsed 35 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00223 0.07934\n",
      "all done trying to increase usage of unit 5155 final usage = 0.98513 6267 sec elapsed 25 9 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00223 0.07912\n",
      "begin usage increase for unit 2373 with usage 0.82711 . Sec, total UU units tried = 6271 90\n",
      "all done trying to increase usage of unit 2373 final usage = 0.98581 6279 sec elapsed 24 22 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00223 0.07887\n",
      "all done trying to increase usage of unit 5213 final usage = 0.98298 6297 sec elapsed 22 11 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00223 0.07864\n",
      "all done trying to increase usage of unit 1457 final usage = 0.98373 6365 sec elapsed 38 7 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07846\n",
      "all done trying to increase usage of unit 2875 final usage = 0.98249 6381 sec elapsed 27 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07796\n",
      "all done trying to increase usage of unit 3549 final usage = 0.98821 6422 sec elapsed 24 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00223 0.07776\n",
      "all done trying to increase usage of unit 4425 final usage = 0.98451 6436 sec elapsed 21 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07749\n",
      "all done trying to increase usage of unit 6942 final usage = 0.99035 6483 sec elapsed 28 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00223 0.07727\n",
      "all done trying to increase usage of unit 6050 final usage = 0.98305 6575 sec elapsed 27 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.0768\n",
      "all done trying to increase usage of unit 3159 final usage = 0.98171 6583 sec elapsed 30 4 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00223 0.07663\n",
      "all done trying to increase usage of unit 1364 final usage = 0.98101 6647 sec elapsed 30 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07626\n",
      "begin usage increase for unit 1496 with usage 0.82994 . Sec, total UU units tried = 6651 100\n",
      "all done trying to increase usage of unit 1496 final usage = 0.98085 6671 sec elapsed 22 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07602\n",
      "all done trying to increase usage of unit 3782 final usage = 0.98276 6771 sec elapsed 30 2 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00223 0.07576\n",
      "all done trying to increase usage of unit 3612 final usage = 0.98704 6837 sec elapsed 28 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07552\n",
      "all done trying to increase usage of unit 5297 final usage = 0.98492 6894 sec elapsed 27 19 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00223 0.07514\n",
      "all done trying to increase usage of unit 1952 final usage = 0.98102 6909 sec elapsed 26 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.07473\n",
      "all done trying to increase usage of unit 2556 final usage = 0.98028 6942 sec elapsed 25 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07449\n",
      "all done trying to increase usage of unit 2278 final usage = 0.98021 6956 sec elapsed 23 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07422\n",
      "all done trying to increase usage of unit 2901 final usage = 0.98236 6972 sec elapsed 26 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07374\n",
      "all done trying to increase usage of unit 4236 final usage = 0.98092 7012 sec elapsed 28 3 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00223 0.0733\n",
      "all done trying to increase usage of unit 3971 final usage = 0.98539 7087 sec elapsed 24 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.0731\n",
      "begin usage increase for unit 5540 with usage 0.83713 . Sec, total UU units tried = 7091 110\n",
      "all done trying to increase usage of unit 5540 final usage = 0.98434 7109 sec elapsed 27 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07285\n",
      "all done trying to increase usage of unit 3610 final usage = 0.98511 7120 sec elapsed 26 10 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07267\n",
      "all done trying to increase usage of unit 1950 final usage = 0.98314 7128 sec elapsed 24 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07224\n",
      "all done trying to increase usage of unit 7664 final usage = 0.98383 7143 sec elapsed 22 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07198\n",
      "all done trying to increase usage of unit 6593 final usage = 0.98589 7223 sec elapsed 31 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07188\n",
      "all done trying to increase usage of unit 1738 final usage = 0.98041 7346 sec elapsed 24 15 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07173\n",
      "all done trying to increase usage of unit 6112 final usage = 0.98138 7354 sec elapsed 20 6 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.07144\n",
      "all done trying to increase usage of unit 2828 final usage = 0.94972 7363 sec elapsed 20 20 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00223 0.07125\n",
      "all done trying to increase usage of unit 7716 final usage = 0.98038 7369 sec elapsed 20 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.0711\n",
      "all done trying to increase usage of unit 1363 final usage = 0.98151 7441 sec elapsed 28 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.07078\n",
      "begin usage increase for unit 8598 with usage 0.84125 . Sec, total UU units tried = 7445 120\n",
      "all done trying to increase usage of unit 8598 final usage = 0.9833 7602 sec elapsed 25 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.07065\n",
      "all done trying to increase usage of unit 8466 final usage = 0.98351 7645 sec elapsed 35 7 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00223 0.07045\n",
      "all done trying to increase usage of unit 7456 final usage = 0.98197 7673 sec elapsed 24 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.07025\n",
      "all done trying to increase usage of unit 2008 final usage = 0.98235 7696 sec elapsed 27 3 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00223 0.07004\n",
      "all done trying to increase usage of unit 907 final usage = 0.98417 7712 sec elapsed 27 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.06986\n",
      "all done trying to increase usage of unit 3933 final usage = 0.98286 7735 sec elapsed 22 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00223 0.06944\n",
      "all done trying to increase usage of unit 1399 final usage = 0.98477 7743 sec elapsed 24 2 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00223 0.06931\n",
      "all done trying to increase usage of unit 5215 final usage = 0.98239 7751 sec elapsed 23 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00223 0.0692\n",
      "all done trying to increase usage of unit 4056 final usage = 0.98022 7798 sec elapsed 20 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06907\n",
      "all done trying to increase usage of unit 141 final usage = 0.98087 7879 sec elapsed 21 18 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06893\n",
      "begin usage increase for unit 901 with usage 0.8449 . Sec, total UU units tried = 7884 130\n",
      "all done trying to increase usage of unit 901 final usage = 0.98168 7892 sec elapsed 20 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06867\n",
      "all done trying to increase usage of unit 1326 final usage = 0.98101 7910 sec elapsed 26 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06846\n",
      "all done trying to increase usage of unit 2124 final usage = 0.98365 8042 sec elapsed 26 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06834\n",
      "all done trying to increase usage of unit 4638 final usage = 0.98219 8059 sec elapsed 23 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.06818\n",
      "all done trying to increase usage of unit 2248 final usage = 0.98504 8129 sec elapsed 26 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06787\n",
      "all done trying to increase usage of unit 8335 final usage = 0.98271 8203 sec elapsed 24 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00222 0.06778\n",
      "all done trying to increase usage of unit 2835 final usage = 0.98438 8223 sec elapsed 23 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.06741\n",
      "all done trying to increase usage of unit 8233 final usage = 0.98411 8243 sec elapsed 23 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06719\n",
      "all done trying to increase usage of unit 1691 final usage = 0.98094 8282 sec elapsed 26 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06687\n",
      "all done trying to increase usage of unit 89 final usage = 0.98036 8347 sec elapsed 25 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00222 0.06664\n",
      "begin usage increase for unit 6962 with usage 0.84867 . Sec, total UU units tried = 8350 140\n",
      "all done trying to increase usage of unit 6962 final usage = 0.98387 8380 sec elapsed 21 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00221 0.06643\n",
      "all done trying to increase usage of unit 2227 final usage = 0.9806 8408 sec elapsed 23 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.06617\n",
      "all done trying to increase usage of unit 1695 final usage = 0.98114 8479 sec elapsed 26 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.06592\n",
      "all done trying to increase usage of unit 2824 final usage = 0.98741 8619 sec elapsed 22 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.06581\n",
      "all done trying to increase usage of unit 4822 final usage = 0.98727 8631 sec elapsed 21 3 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00222 0.06574\n",
      "all done trying to increase usage of unit 1747 final usage = 0.98523 8646 sec elapsed 22 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.06558\n",
      "all done trying to increase usage of unit 4820 final usage = 0.98398 8660 sec elapsed 22 15 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.0655\n",
      "all done trying to increase usage of unit 6075 final usage = 0.98135 8729 sec elapsed 22 1 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00221 0.06534\n",
      "all done trying to increase usage of unit 118 final usage = 0.9801 8767 sec elapsed 25 2 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00222 0.06507\n",
      "all done trying to increase usage of unit 3055 final usage = 0.9821 8781 sec elapsed 19 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.06484\n"
     ]
    }
   ],
   "source": [
    "#NEW for WI - faster: use the avoidedEnclave, avoidedIsland subroutines vs. wontEnclave\n",
    "\n",
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overused first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used.  Using new-for-WI avoidedEnclave routines\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        complementContig = avoidedEnclave(HDunitList[t], unitNbrs, UUUc)\n",
    "        #contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not complementContig: #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            complementContig  = avoidedEnclave(HDunitList[t], unitNbrs, [UUU])\n",
    "            loopUUUc = [UUU]\n",
    "        if complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = avoidedEnclave(list(trySet), unitNbrs, [addU])\n",
    "                    #cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        cContig = avoidedIsland(list(trySet), unitNbrs, [shedU])\n",
    "                        #contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if cContig: # and contig :\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00226 1.00221 and their SDs are 0.11998 0.06484\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 9129\n",
      "working on HD 500 out of 9129\n",
      "working on HD 1000 out of 9129\n",
      "working on HD 1500 out of 9129\n",
      "working on HD 2000 out of 9129\n",
      "working on HD 2500 out of 9129\n",
      "working on HD 3000 out of 9129\n",
      "working on HD 3500 out of 9129\n",
      "working on HD 4000 out of 9129\n",
      "working on HD 4500 out of 9129\n",
      "working on HD 5000 out of 9129\n",
      "working on HD 5500 out of 9129\n",
      "working on HD 6000 out of 9129\n",
      "working on HD 6500 out of 9129\n",
      "working on HD 7000 out of 9129\n",
      "working on HD 7500 out of 9129\n",
      "working on HD 8000 out of 9129\n",
      "working on HD 8500 out of 9129\n",
      "working on HD 9000 out of 9129\n",
      "all done checking HD and complement contiguity for all 9129 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "patchUpHDunitList = [HDunitList[t].copy() for t in range(nHDs)]  #more safekeeping for later contig troubleshooting\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "1ee72cf0-618e-411f-aa37-518ac2c679ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "#optional intermediate save\n",
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"underPatched.csv\" #\"contigUnpatchedB.csv\"  #underPatched\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter max std devn; e.g.0.04 0.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.02\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 3046 units out of 9024 with usage above 1.02\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 38017\n",
      "current avg and SD of unit usage are 1.00221 0.06484 . Now trying to reduce up to 3046 units' overusage\n",
      "starting to reduce usage for unit 6705 with usage 1.59629 . Total units tried = 1\n",
      "try to drop unit 6705 from 24 th HD.  usage down to 1.5017022894528804\n",
      "try to drop unit 6705 from 48 th HD.  usage down to 1.4137941404194656\n",
      "try to drop unit 6705 from 72 th HD.  usage down to 1.2879429609165365\n",
      "try to drop unit 6705 from 96 th HD.  usage down to 1.2170953661725947\n",
      "try to drop unit 6705 from 120 th HD.  usage down to 1.0912165678259966\n",
      "all done trying to reduce usage of unit 6705 final usage = 1.07228 69 sec elapsed 89 2 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00217 0.0621\n",
      "starting to reduce usage for unit 7308 with usage 1.53587 . Total units tried = 2\n",
      "try to drop unit 7308 from 25 th HD.  usage down to 1.5000254310872445\n",
      "try to drop unit 7308 from 50 th HD.  usage down to 1.4170518487894264\n",
      "try to drop unit 7308 from 75 th HD.  usage down to 1.345295462570453\n",
      "try to drop unit 7308 from 100 th HD.  usage down to 1.3086926036104023\n",
      "try to drop unit 7308 from 125 th HD.  usage down to 1.2915334105935548\n",
      "all done trying to reduce usage of unit 7308 final usage = 1.29153 84 sec elapsed 41 0 successful, failed patches, couldn't start= 85\n",
      "current avg and SD of usage are 1.00214 0.06121\n",
      "starting to reduce usage for unit 8384 with usage 1.53761 . Total units tried = 3\n",
      "try to drop unit 8384 from 24 th HD.  usage down to 1.4505732339057427\n",
      "try to drop unit 8384 from 48 th HD.  usage down to 1.4065895677708247\n",
      "try to drop unit 8384 from 72 th HD.  usage down to 1.3445852637331874\n",
      "try to drop unit 8384 from 96 th HD.  usage down to 1.3145898843252635\n",
      "try to drop unit 8384 from 120 th HD.  usage down to 1.2466291163362853\n",
      "all done trying to reduce usage of unit 8384 final usage = 1.22571 149 sec elapsed 53 1 successful, failed patches, couldn't start= 71\n",
      "current avg and SD of usage are 1.00213 0.05985\n",
      "starting to reduce usage for unit 2978 with usage 1.41118 . Total units tried = 4\n",
      "try to drop unit 2978 from 20 th HD.  usage down to 1.329617165647554\n",
      "try to drop unit 2978 from 40 th HD.  usage down to 1.2560944886167815\n",
      "try to drop unit 2978 from 60 th HD.  usage down to 1.155089746951993\n",
      "try to drop unit 2978 from 80 th HD.  usage down to 1.0639304654638084\n",
      "all done trying to reduce usage of unit 2978 final usage = 1.01781 192 sec elapsed 65 2 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.0021 0.05829\n",
      "starting to reduce usage for unit 1228 with usage 1.37862 . Total units tried = 5\n",
      "try to drop unit 1228 from 23 th HD.  usage down to 1.3382355600553817\n",
      "try to drop unit 1228 from 46 th HD.  usage down to 1.3361194305570976\n",
      "try to drop unit 1228 from 69 th HD.  usage down to 1.318634072148349\n",
      "try to drop unit 1228 from 92 th HD.  usage down to 1.2846286996762442\n",
      "try to drop unit 1228 from 115 th HD.  usage down to 1.2620101818940948\n",
      "all done trying to reduce usage of unit 1228 final usage = 1.26201 201 sec elapsed 20 0 successful, failed patches, couldn't start= 100\n",
      "current avg and SD of usage are 1.00209 0.05776\n",
      "starting to reduce usage for unit 6079 with usage 1.30245 . Total units tried = 6\n",
      "try to drop unit 6079 from 21 th HD.  usage down to 1.2025126167151752\n",
      "try to drop unit 6079 from 42 th HD.  usage down to 1.1209304980650936\n",
      "try to drop unit 6079 from 63 th HD.  usage down to 1.037762268678555\n",
      "all done trying to reduce usage of unit 6079 final usage = 1.0188 216 sec elapsed 48 0 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00206 0.05676\n",
      "starting to reduce usage for unit 644 with usage 1.29942 . Total units tried = 7\n",
      "try to drop unit 644 from 22 th HD.  usage down to 1.2025560177552812\n",
      "try to drop unit 644 from 44 th HD.  usage down to 1.05309335728115\n",
      "all done trying to reduce usage of unit 644 final usage = 1.01281 229 sec elapsed 43 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00204 0.05593\n",
      "starting to reduce usage for unit 3297 with usage 1.32774 . Total units tried = 8\n",
      "try to drop unit 3297 from 20 th HD.  usage down to 1.2328104882204352\n",
      "try to drop unit 3297 from 40 th HD.  usage down to 1.216183944331497\n",
      "try to drop unit 3297 from 60 th HD.  usage down to 1.2119306424064187\n",
      "try to drop unit 3297 from 80 th HD.  usage down to 1.2054204863986457\n",
      "try to drop unit 3297 from 100 th HD.  usage down to 1.1925987670209128\n",
      "all done trying to reduce usage of unit 3297 final usage = 1.18296 330 sec elapsed 25 0 successful, failed patches, couldn't start= 78\n",
      "current avg and SD of usage are 1.00203 0.05539\n",
      "starting to reduce usage for unit 8718 with usage 1.27791 . Total units tried = 9\n",
      "try to drop unit 8718 from 24 th HD.  usage down to 1.2779094295664621\n",
      "try to drop unit 8718 from 48 th HD.  usage down to 1.2779094295664621\n",
      "try to drop unit 8718 from 72 th HD.  usage down to 1.2779094295664621\n",
      "try to drop unit 8718 from 96 th HD.  usage down to 1.2779094295664621\n",
      "try to drop unit 8718 from 120 th HD.  usage down to 1.2779094295664621\n",
      "all done trying to reduce usage of unit 8718 final usage = 1.27791 339 sec elapsed 0 0 successful, failed patches, couldn't start= 125\n",
      "current avg and SD of usage are 1.00203 0.05539\n",
      "starting to reduce usage for unit 8715 with usage 1.27791 . Total units tried = 10\n",
      "try to drop unit 8715 from 22 th HD.  usage down to 1.208085047221301\n",
      "try to drop unit 8715 from 44 th HD.  usage down to 1.123709479811503\n",
      "try to drop unit 8715 from 66 th HD.  usage down to 1.0466949918310997\n",
      "all done trying to reduce usage of unit 8715 final usage = 1.01595 362 sec elapsed 52 4 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00201 0.05447\n",
      "starting to reduce usage for unit 1377 with usage 1.27445 . Total units tried = 11\n",
      "try to drop unit 1377 from 21 th HD.  usage down to 1.154543945993701\n",
      "try to drop unit 1377 from 42 th HD.  usage down to 1.0544861361118767\n",
      "all done trying to reduce usage of unit 1377 final usage = 1.01657 389 sec elapsed 41 2 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.002 0.05366\n",
      "starting to reduce usage for unit 4954 with usage 1.26201 . Total units tried = 12\n",
      "try to drop unit 4954 from 18 th HD.  usage down to 1.1910928824494451\n",
      "try to drop unit 4954 from 36 th HD.  usage down to 1.1105444976624057\n",
      "try to drop unit 4954 from 54 th HD.  usage down to 1.0274038871195534\n",
      "all done trying to reduce usage of unit 4954 final usage = 1.0154 406 sec elapsed 40 0 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00198 0.05286\n",
      "starting to reduce usage for unit 4617 with usage 1.36554 . Total units tried = 13\n",
      "try to drop unit 4617 from 19 th HD.  usage down to 1.2460951520254075\n",
      "try to drop unit 4617 from 38 th HD.  usage down to 1.1447155831965263\n",
      "all done trying to reduce usage of unit 4617 final usage = 1.01636 417 sec elapsed 56 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00199 0.05227\n",
      "starting to reduce usage for unit 185 with usage 1.22132 . Total units tried = 14\n",
      "try to drop unit 185 from 21 th HD.  usage down to 1.1875984411337757\n",
      "try to drop unit 185 from 42 th HD.  usage down to 1.1576832878908019\n",
      "try to drop unit 185 from 63 th HD.  usage down to 1.1049523394108998\n",
      "try to drop unit 185 from 84 th HD.  usage down to 1.0614723883771917\n",
      "try to drop unit 185 from 105 th HD.  usage down to 1.032693553273747\n",
      "all done trying to reduce usage of unit 185 final usage = 1.02921 446 sec elapsed 35 6 successful, failed patches, couldn't start= 68\n",
      "current avg and SD of usage are 1.00198 0.0518\n",
      "starting to reduce usage for unit 8406 with usage 1.21722 . Total units tried = 15\n",
      "try to drop unit 8406 from 19 th HD.  usage down to 1.156756083853243\n",
      "try to drop unit 8406 from 38 th HD.  usage down to 1.098484269260054\n",
      "try to drop unit 8406 from 57 th HD.  usage down to 1.036937648917511\n",
      "all done trying to reduce usage of unit 8406 final usage = 1.01622 453 sec elapsed 31 0 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00196 0.05109\n",
      "starting to reduce usage for unit 4261 with usage 1.18886 . Total units tried = 16\n",
      "try to drop unit 4261 from 18 th HD.  usage down to 1.088068019647667\n",
      "all done trying to reduce usage of unit 4261 final usage = 1.01763 465 sec elapsed 28 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00195 0.05062\n",
      "starting to reduce usage for unit 8566 with usage 1.19271 . Total units tried = 17\n",
      "try to drop unit 8566 from 19 th HD.  usage down to 1.0930893884634107\n",
      "all done trying to reduce usage of unit 8566 final usage = 1.01145 478 sec elapsed 29 1 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00193 0.05015\n",
      "starting to reduce usage for unit 8534 with usage 1.22571 . Total units tried = 18\n",
      "try to drop unit 8534 from 17 th HD.  usage down to 1.122867762670997\n",
      "try to drop unit 8534 from 34 th HD.  usage down to 1.0257783208920914\n",
      "all done trying to reduce usage of unit 8534 final usage = 1.01535 503 sec elapsed 33 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00191 0.04969\n",
      "starting to reduce usage for unit 8868 with usage 1.17766 . Total units tried = 19\n",
      "try to drop unit 8868 from 25 th HD.  usage down to 1.1666081199452416\n",
      "try to drop unit 8868 from 50 th HD.  usage down to 1.1564075603500374\n",
      "try to drop unit 8868 from 75 th HD.  usage down to 1.1564075603500374\n",
      "try to drop unit 8868 from 100 th HD.  usage down to 1.1564075603500374\n",
      "try to drop unit 8868 from 125 th HD.  usage down to 1.1564075603500374\n",
      "all done trying to reduce usage of unit 8868 final usage = 1.15641 509 sec elapsed 4 0 successful, failed patches, couldn't start= 124\n",
      "current avg and SD of usage are 1.00191 0.04962\n",
      "starting to reduce usage for unit 4705 with usage 1.1764 . Total units tried = 20\n",
      "try to drop unit 4705 from 21 th HD.  usage down to 1.1265897306514574\n",
      "try to drop unit 4705 from 42 th HD.  usage down to 1.060283462916549\n",
      "all done trying to reduce usage of unit 4705 final usage = 1.01603 515 sec elapsed 38 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.0019 0.04915\n",
      "starting to reduce usage for unit 6472 with usage 1.17097 . Total units tried = 21\n",
      "try to drop unit 6472 from 17 th HD.  usage down to 1.0852719405218965\n",
      "all done trying to reduce usage of unit 6472 final usage = 1.01934 520 sec elapsed 22 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00188 0.04876\n",
      "starting to reduce usage for unit 7313 with usage 1.16409 . Total units tried = 22\n",
      "try to drop unit 7313 from 20 th HD.  usage down to 1.099535100502617\n",
      "try to drop unit 7313 from 40 th HD.  usage down to 1.0609331633341181\n",
      "all done trying to reduce usage of unit 7313 final usage = 1.01696 541 sec elapsed 27 1 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 1.00187 0.04851\n",
      "starting to reduce usage for unit 1855 with usage 1.16389 . Total units tried = 23\n",
      "try to drop unit 1855 from 22 th HD.  usage down to 1.1041224589178682\n",
      "try to drop unit 1855 from 44 th HD.  usage down to 1.069517362983237\n",
      "try to drop unit 1855 from 66 th HD.  usage down to 1.0244683985925025\n",
      "all done trying to reduce usage of unit 1855 final usage = 1.01805 554 sec elapsed 21 0 successful, failed patches, couldn't start= 49\n",
      "current avg and SD of usage are 1.00185 0.04805\n",
      "starting to reduce usage for unit 677 with usage 1.21908 . Total units tried = 24\n",
      "try to drop unit 677 from 18 th HD.  usage down to 1.1321332271305653\n",
      "try to drop unit 677 from 36 th HD.  usage down to 1.0685678008341035\n",
      "all done trying to reduce usage of unit 677 final usage = 1.01661 560 sec elapsed 27 1 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00183 0.04729\n",
      "starting to reduce usage for unit 7502 with usage 1.16272 . Total units tried = 25\n",
      "try to drop unit 7502 from 9 th HD.  usage down to 1.1277957534914251\n",
      "try to drop unit 7502 from 18 th HD.  usage down to 1.0648655605993602\n",
      "all done trying to reduce usage of unit 7502 final usage = 1.01974 587 sec elapsed 25 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00182 0.04691\n",
      "starting to reduce usage for unit 6791 with usage 1.18296 . Total units tried = 26\n",
      "try to drop unit 6791 from 10 th HD.  usage down to 1.1536930225720552\n",
      "try to drop unit 6791 from 20 th HD.  usage down to 1.101922157705423\n",
      "try to drop unit 6791 from 30 th HD.  usage down to 1.0320030821819766\n",
      "all done trying to reduce usage of unit 6791 final usage = 1.0117 597 sec elapsed 25 2 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00181 0.04645\n",
      "starting to reduce usage for unit 2916 with usage 1.1587 . Total units tried = 27\n",
      "try to drop unit 2916 from 19 th HD.  usage down to 1.1586972940084992\n",
      "try to drop unit 2916 from 38 th HD.  usage down to 1.1586972940084992\n",
      "try to drop unit 2916 from 57 th HD.  usage down to 1.1586972940084992\n",
      "try to drop unit 2916 from 76 th HD.  usage down to 1.1586972940084992\n",
      "try to drop unit 2916 from 95 th HD.  usage down to 1.1586972940084992\n",
      "all done trying to reduce usage of unit 2916 final usage = 1.1587 606 sec elapsed 0 0 successful, failed patches, couldn't start= 98\n",
      "current avg and SD of usage are 1.00181 0.04645\n",
      "starting to reduce usage for unit 1542 with usage 1.15844 . Total units tried = 28\n",
      "try to drop unit 1542 from 18 th HD.  usage down to 1.0538022409352417\n",
      "all done trying to reduce usage of unit 1542 final usage = 1.01882 612 sec elapsed 23 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00179 0.04599\n",
      "starting to reduce usage for unit 5728 with usage 1.15987 . Total units tried = 29\n",
      "try to drop unit 5728 from 22 th HD.  usage down to 1.0753160049707047\n",
      "try to drop unit 5728 from 44 th HD.  usage down to 1.0655902264500057\n",
      "try to drop unit 5728 from 66 th HD.  usage down to 1.0655902264500057\n",
      "try to drop unit 5728 from 88 th HD.  usage down to 1.0655902264500057\n",
      "try to drop unit 5728 from 110 th HD.  usage down to 1.0655902264500057\n",
      "all done trying to reduce usage of unit 5728 final usage = 1.06085 619 sec elapsed 18 0 successful, failed patches, couldn't start= 97\n",
      "current avg and SD of usage are 1.00178 0.04574\n",
      "starting to reduce usage for unit 275 with usage 1.15602 . Total units tried = 30\n",
      "try to drop unit 275 from 21 th HD.  usage down to 1.1039738432352237\n",
      "try to drop unit 275 from 42 th HD.  usage down to 1.0923883959074518\n",
      "try to drop unit 275 from 63 th HD.  usage down to 1.0823206697984658\n",
      "try to drop unit 275 from 84 th HD.  usage down to 1.037180957778529\n",
      "try to drop unit 275 from 105 th HD.  usage down to 1.0212251572358466\n",
      "all done trying to reduce usage of unit 275 final usage = 1.01718 636 sec elapsed 27 6 successful, failed patches, couldn't start= 72\n",
      "current avg and SD of usage are 1.00178 0.04542\n",
      "starting to reduce usage for unit 7072 with usage 1.15328 . Total units tried = 31\n",
      "try to drop unit 7072 from 21 th HD.  usage down to 1.1301643981319502\n",
      "try to drop unit 7072 from 42 th HD.  usage down to 1.12304531238042\n",
      "try to drop unit 7072 from 63 th HD.  usage down to 1.12304531238042\n",
      "try to drop unit 7072 from 84 th HD.  usage down to 1.1089057796047566\n",
      "try to drop unit 7072 from 105 th HD.  usage down to 1.1089057796047566\n",
      "all done trying to reduce usage of unit 7072 final usage = 1.10891 652 sec elapsed 9 2 successful, failed patches, couldn't start= 95\n",
      "current avg and SD of usage are 1.00178 0.04532\n",
      "starting to reduce usage for unit 5725 with usage 1.15763 . Total units tried = 32\n",
      "try to drop unit 5725 from 19 th HD.  usage down to 1.1540099816828353\n",
      "try to drop unit 5725 from 38 th HD.  usage down to 1.1540099816828353\n",
      "try to drop unit 5725 from 57 th HD.  usage down to 1.1303656211358257\n",
      "try to drop unit 5725 from 76 th HD.  usage down to 1.1253560889674203\n",
      "try to drop unit 5725 from 95 th HD.  usage down to 1.1253560889674203\n",
      "all done trying to reduce usage of unit 5725 final usage = 1.12536 660 sec elapsed 7 0 successful, failed patches, couldn't start= 92\n",
      "current avg and SD of usage are 1.00178 0.04522\n",
      "starting to reduce usage for unit 2295 with usage 1.13971 . Total units tried = 33\n",
      "try to drop unit 2295 from 24 th HD.  usage down to 1.126835669878377\n",
      "try to drop unit 2295 from 48 th HD.  usage down to 1.126835669878377\n",
      "try to drop unit 2295 from 72 th HD.  usage down to 1.112308158108315\n",
      "try to drop unit 2295 from 96 th HD.  usage down to 1.0974163406384272\n",
      "try to drop unit 2295 from 120 th HD.  usage down to 1.0823982655974727\n",
      "all done trying to reduce usage of unit 2295 final usage = 1.07403 666 sec elapsed 10 0 successful, failed patches, couldn't start= 115\n",
      "current avg and SD of usage are 1.00178 0.04501\n",
      "starting to reduce usage for unit 2948 with usage 1.13849 . Total units tried = 34\n",
      "try to drop unit 2948 from 13 th HD.  usage down to 1.080445218794905\n",
      "all done trying to reduce usage of unit 2948 final usage = 1.01909 672 sec elapsed 19 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00176 0.04466\n",
      "starting to reduce usage for unit 1690 with usage 1.13738 . Total units tried = 35\n",
      "try to drop unit 1690 from 21 th HD.  usage down to 1.119172098351542\n",
      "try to drop unit 1690 from 42 th HD.  usage down to 1.0915243206555163\n",
      "try to drop unit 1690 from 63 th HD.  usage down to 1.088300807044395\n",
      "try to drop unit 1690 from 84 th HD.  usage down to 1.0853337541244887\n",
      "try to drop unit 1690 from 105 th HD.  usage down to 1.0853337541244887\n",
      "all done trying to reduce usage of unit 1690 final usage = 1.08533 679 sec elapsed 9 1 successful, failed patches, couldn't start= 96\n",
      "current avg and SD of usage are 1.00176 0.04449\n",
      "starting to reduce usage for unit 8397 with usage 1.13244 . Total units tried = 36\n",
      "try to drop unit 8397 from 19 th HD.  usage down to 1.0507641681318338\n",
      "all done trying to reduce usage of unit 8397 final usage = 1.01921 684 sec elapsed 23 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00175 0.04422\n",
      "starting to reduce usage for unit 7511 with usage 1.12934 . Total units tried = 37\n",
      "try to drop unit 7511 from 16 th HD.  usage down to 1.0605806942815312\n",
      "all done trying to reduce usage of unit 7511 final usage = 1.01398 690 sec elapsed 18 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00175 0.04395\n",
      "starting to reduce usage for unit 3839 with usage 1.12769 . Total units tried = 38\n",
      "try to drop unit 3839 from 11 th HD.  usage down to 1.057365071768573\n",
      "all done trying to reduce usage of unit 3839 final usage = 1.01681 712 sec elapsed 15 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00174 0.04363\n",
      "starting to reduce usage for unit 7115 with usage 1.1269 . Total units tried = 39\n",
      "try to drop unit 7115 from 14 th HD.  usage down to 1.0640330497401707\n",
      "try to drop unit 7115 from 28 th HD.  usage down to 1.0459335008555415\n",
      "try to drop unit 7115 from 42 th HD.  usage down to 1.0240264970936352\n",
      "all done trying to reduce usage of unit 7115 final usage = 1.01992 718 sec elapsed 19 0 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 1.00174 0.04333\n",
      "starting to reduce usage for unit 7135 with usage 1.13712 . Total units tried = 40\n",
      "try to drop unit 7135 from 11 th HD.  usage down to 1.0708220245507267\n",
      "all done trying to reduce usage of unit 7135 final usage = 1.01374 722 sec elapsed 19 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00172 0.04305\n",
      "starting to reduce usage for unit 417 with usage 1.12349 . Total units tried = 41\n",
      "try to drop unit 417 from 9 th HD.  usage down to 1.1103800997835576\n",
      "try to drop unit 417 from 18 th HD.  usage down to 1.0938942804790326\n",
      "try to drop unit 417 from 27 th HD.  usage down to 1.060059881801077\n",
      "try to drop unit 417 from 36 th HD.  usage down to 1.038956454871646\n",
      "all done trying to reduce usage of unit 417 final usage = 1.01676 752 sec elapsed 19 0 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00171 0.0429\n",
      "starting to reduce usage for unit 7510 with usage 1.12271 . Total units tried = 42\n",
      "try to drop unit 7510 from 21 th HD.  usage down to 1.075927565080549\n",
      "all done trying to reduce usage of unit 7510 final usage = 1.01647 759 sec elapsed 18 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00171 0.04264\n",
      "starting to reduce usage for unit 4021 with usage 1.12218 . Total units tried = 43\n",
      "try to drop unit 4021 from 14 th HD.  usage down to 1.074661043820738\n",
      "all done trying to reduce usage of unit 4021 final usage = 1.02 767 sec elapsed 16 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.0017 0.04237\n",
      "starting to reduce usage for unit 8287 with usage 1.12457 . Total units tried = 44\n",
      "try to drop unit 8287 from 16 th HD.  usage down to 1.0432229086268683\n",
      "all done trying to reduce usage of unit 8287 final usage = 1.01233 772 sec elapsed 17 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00168 0.04218\n",
      "starting to reduce usage for unit 184 with usage 1.11928 . Total units tried = 45\n",
      "try to drop unit 184 from 20 th HD.  usage down to 1.090555030761026\n",
      "try to drop unit 184 from 40 th HD.  usage down to 1.0754317410775216\n",
      "try to drop unit 184 from 60 th HD.  usage down to 1.0661189300288307\n",
      "try to drop unit 184 from 80 th HD.  usage down to 1.0661189300288307\n",
      "try to drop unit 184 from 100 th HD.  usage down to 1.0661189300288307\n",
      "all done trying to reduce usage of unit 184 final usage = 1.05695 782 sec elapsed 8 4 successful, failed patches, couldn't start= 90\n",
      "current avg and SD of usage are 1.00168 0.04203\n",
      "starting to reduce usage for unit 226 with usage 1.11506 . Total units tried = 46\n",
      "try to drop unit 226 from 17 th HD.  usage down to 1.0447945523499889\n",
      "all done trying to reduce usage of unit 226 final usage = 1.01354 792 sec elapsed 23 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00168 0.04178\n",
      "starting to reduce usage for unit 1975 with usage 1.1141 . Total units tried = 47\n",
      "all done trying to reduce usage of unit 1975 final usage = 1.01448 797 sec elapsed 15 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00167 0.04151\n",
      "starting to reduce usage for unit 6464 with usage 1.11297 . Total units tried = 48\n",
      "try to drop unit 6464 from 11 th HD.  usage down to 1.0508049388056124\n",
      "all done trying to reduce usage of unit 6464 final usage = 1.01516 807 sec elapsed 16 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00166 0.04125\n",
      "starting to reduce usage for unit 7231 with usage 1.11221 . Total units tried = 49\n",
      "all done trying to reduce usage of unit 7231 final usage = 1.01891 815 sec elapsed 17 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00165 0.04098\n",
      "starting to reduce usage for unit 3016 with usage 1.11128 . Total units tried = 50\n",
      "try to drop unit 3016 from 19 th HD.  usage down to 1.0792904880929906\n",
      "try to drop unit 3016 from 38 th HD.  usage down to 1.0610449538918167\n",
      "try to drop unit 3016 from 57 th HD.  usage down to 1.0366509390166423\n",
      "all done trying to reduce usage of unit 3016 final usage = 1.01861 823 sec elapsed 15 0 successful, failed patches, couldn't start= 48\n",
      "current avg and SD of usage are 1.00164 0.04079\n",
      "starting to reduce usage for unit 1819 with usage 1.11007 . Total units tried = 51\n",
      "try to drop unit 1819 from 9 th HD.  usage down to 1.0493464008233255\n",
      "all done trying to reduce usage of unit 1819 final usage = 1.01481 828 sec elapsed 14 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00163 0.04053\n",
      "starting to reduce usage for unit 502 with usage 1.10994 . Total units tried = 52\n",
      "try to drop unit 502 from 16 th HD.  usage down to 1.044142221566152\n",
      "all done trying to reduce usage of unit 502 final usage = 1.01881 836 sec elapsed 14 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00163 0.04032\n",
      "starting to reduce usage for unit 730 with usage 1.11431 . Total units tried = 53\n",
      "try to drop unit 730 from 20 th HD.  usage down to 1.1075064248588724\n",
      "try to drop unit 730 from 40 th HD.  usage down to 1.1075064248588724\n",
      "try to drop unit 730 from 60 th HD.  usage down to 1.1075064248588724\n",
      "try to drop unit 730 from 80 th HD.  usage down to 1.1061241674923736\n",
      "try to drop unit 730 from 100 th HD.  usage down to 1.1061241674923736\n",
      "all done trying to reduce usage of unit 730 final usage = 1.09813 844 sec elapsed 3 0 successful, failed patches, couldn't start= 98\n",
      "current avg and SD of usage are 1.00163 0.0403\n",
      "starting to reduce usage for unit 5623 with usage 1.10848 . Total units tried = 54\n",
      "try to drop unit 5623 from 17 th HD.  usage down to 1.0783527625912184\n",
      "try to drop unit 5623 from 34 th HD.  usage down to 1.0418130324166948\n",
      "all done trying to reduce usage of unit 5623 final usage = 1.01721 857 sec elapsed 17 0 successful, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 1.00162 0.04008\n",
      "starting to reduce usage for unit 149 with usage 1.10259 . Total units tried = 55\n",
      "try to drop unit 149 from 13 th HD.  usage down to 1.070883838153213\n",
      "all done trying to reduce usage of unit 149 final usage = 1.01854 873 sec elapsed 13 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00161 0.03987\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run second for most states except MA, WA\n",
    "maxSD = 0.04 #0.06  #0.08  #0.04\n",
    "maxSD = float(input(\"enter max std devn; e.g.\"+str(r3(maxSD)) ) )\n",
    "stopMaxUse = 1.+  0.5 * maxSD\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists) #work from farthest HD in\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            legitSwap = False\n",
    "            if abs(currPop - aDP) <= 2.* maxGap: #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f90621-e7fc-4891-a5cd-69033c3c48e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy one of two above blocks and run again if needed (n/a for NC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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C0f33399vq5fBYIDdbofRaERHRweee+45tLa24h8Z/0Dpj0v7PdaZl2JqaipOnTrVu10URSxYsAAbNmzwz5MIM9sON2Bmdni8Polo5Azl89vnfULCnVopoqXDheb2brlLGREvvfQSJEk65+fMzLd2ux1utxtJSUkAgGUnliHvtTzkvp6LK966FIWFhXj88cfR3t7ee8yTJ0/2OZbH42EAISIKQQwhfrB0YiK+OtaEdqdb7lICxnXXXYfMzEwoJSWuO3UdVgorcav9x9ixYwcA+GzyOyIiCh7sE+IHgiBgRrYF35xqxbQx4T2j6hmiKOKWW27Btm3bYLVa4fV6oTFqoNPpcNVVV0EUmYeJiMINQ4ifRGqUcLo5muO7Zs6cKXcJREQUIPj10488noDq80tERBRQGEL8qNvjhZfryhAREfWLIcSPrhhtxq7KZrnLICIiCkgMIX6050QLpqRHy10GERFRQGII8ZP9p1oRZ9BApeApJiIi6g8/If2ksc2J/NQoucsgIiIKWAwhfnC0oQ3JURFyl0FERBTQGEL8oK3LDYOWU7AQERFdCEOIHzS1O5Fo4jTkFJgCbM1KIgpjDCF+4PX2TN1OFIj42iSiQMEQ4geZcXrsq2qRuwwiIqKAxhDiBxmxkah3OOUug6hfvBxDRIGCIcRPDFolalo75S6DiIgoYDGE+Mm0MbHYf6pV7jKIiIgCFkOIH2lUCi5gRwGHHVOJKFAwhPhRfkoUPiyp5TV4IiKifjCE+FFMpBozsy3YdrhB7lKIejEUE1GgYAjxM5NOBZNOhR0VjXKXQkREFFAYQkbA5LRotHd75C6DiIgooDCEjJB0cwSON7bLXQYREVHAYAgZIdnxBlQ2MYSQ/Dg6hogCBUPICPJ42CGQ5MeOqUQUKBhCRsieE83ISzHJXQYRDFolisrr4eEcNkQkM6XcBYSDTw/WISZSjXijVu5SiDA1PQbtTjc2H6hDnFGDKWnRcpdERGGKLSEjwO2VMC7BKHcZRL0iNUosyk1AokmLjaW1ONbQJndJRBSGGEJGwJVZsdhaXo/DdQ65SyHqI9Gkw6LcRHR7vPi4pBZNbVz9mYhGDkPICIhQK3FVXiK63V5sPlCHjm633CUR9TEuwYjFuQmoqG/D5gN16HJxXhsi8j+GkBGUm2zC3HFx2Fhqhcvjlbscoj4EQcDlo82YNdaCHUcb8UVFI0fSEJFfMYSMMFEUcFVeIj4/0oi9VS1yl0N0DpVCxJxx8chNNqGwrA6l1Ta5SyKiEMUQIgOtSoHZ4+KgEkVsPVSPAzV2uUsiOodJp8Ki3ASYdCpsKKnFyeYOuUsiohDDECKjvBRTTxhRCPjgmxo2fVNASo2JwOK8RLR2uLCxtBa2TpfcJRFRiOA8IQEgK94As16DwjIrkqJ0mJgSJXdJROfISzEhN9mIHUeb4HR7cGWWBSoFv8cQ0dDxHSRAxESqsSg3EXEGLT7cX4OKeg7npcAjCAKmZ8Zi2phYFJU34MtjTWzBI6IhYwgJMAkmLb4/MQn1Dif7ilDA0qoUmJ8Tj6w4PQrLrCi3MjQT0eAxhASoaWNi4fFKOFjLIEKBy6zXYFFuIlQKARtLa2G1dcldEhEFEYaQAJaXYoLT7eUQSQp4oy16LMpNRI2tE4VlVrQ7OSEfEV0cQ0iAm5QaBZfHi08P1vHaOwW8KWnRmDc+HrtPtHClXiK6KIaQIDA5LRpZcQZO+U5BQSEKmJltwWWjYvDJwTpOykdE58UhukEizRyBxCgtPq9ohEIQ8L3MWIiiIHdZROcVoVZi4YQE1Nm7sLHUiqx4PcZY9HKXRUQBhC0hQUSlEDF7bBwmp0WhsMyKz4808to7Bbx4oxaLchPg9kjYwJV6iegsghRgHQ3sdjtMJhNsNhuMRqPc5QS0LpcH+6pa0e50Izlah/GJPF8U2CRJwtfHm+HocmNGtgVqJb8HEYWKoXx+83JMENOqFCgYYwYAHG1oQ2GZFeMTjEgzR8hcGVH/zqzU2+324rMjDYjUKHH5qBgIAi8tEoUjfg0JEWMseiyckIBdlc1oZHM3BTi1UsTc8d9OdnakjpOdEYUjhpAQc+3UFHxRwb4iFBzOTHYmCMCGkloGaKIwwxASgpZOTMKnh+rlLoNowDLjDFiUm4Cj9W345EAdut1euUsiohHAPiEhSBQF6FQKucsgGhT2FyEKP2wJCVGOLhe/TVJQYn8RovDBIbohyuXx4ouKRgiCgA6nG2nmCExIMsldFtGgVdQ7cKSuDZeOikGsXiN3OUR0HhyiS71UChGzxsYB6Jmb4eMSK1QKEdnxBpkrIxqczDgDxlj0+Pp4M4qrWjm/CFEIYQgJAw6nGzWtnfheVqzcpRANCfuLEIUmfp0IA0atCgsmxKOysV3uUoiGhf1FiEILQ0iYSDdH4mhDGzq7PXKXQjRsnF+EKDQwhISRdHMknG6GEAodnF+EKLgNKoS8/PLLmDhxIoxGI4xGIwoKCrBhw4be+61WK370ox8hISEBkZGRmDJlCv7xj3/4vGgamub2bkRFqOUug8inzvQXmZFtwWdHGvDlsSYE2KA/IjqPQYWQlJQUPPvss9izZw92796NOXPmYNmyZSgrKwMA3HrrrSgvL8cHH3yAkpISXHPNNbjhhhuwb98+vxRPA3eyuQOJJq3cZRD5DfuLEAWfQYWQpUuX4qqrrkJWVhays7Px1FNPQa/X48svvwQA7NixAw888AAuu+wyjB49Go899hiioqKwZ88evxRPA1dWY0Nu8uDnCdm+fTuWLl2KpKQkCIKA999//4L719bW4qabbkJ2djZEUcSKFSv63a+1tRX33XcfEhMTodFokJ2djY8//njQ9RF913f7izS3d8tdEhGdx5D7hHg8Hqxduxbt7e0oKCgAAEybNg1///vf0dzcDK/Xi7Vr16KrqwuzZs3yVb00BHurWjApNRoV9Q5sLa9HUXk9th1uwKmWjos+tr29Hfn5+Vi9evWAfpfT6YTFYsFjjz2G/Pz8fvfp7u7G/PnzUVlZiXXr1qG8vBx/+ctfkJycPKjnRXQhZ/qLHK5z4NODdXB52F+EKNAMep6QkpISFBQUoKurC3q9HuvXr0dOTg4A4J133sGNN94Is9kMpVKJiIgIrF+/HpmZmec9ntPphNP5bc92u90+hKdB53OsoQ2Hah2I0qnw1fFm3Hx5OgDA65VQ0dDTmS8qQoVLMmL6ffzixYuxePHiAf++jIwMvPjiiwCAV199td99Xn31VTQ3N2PHjh1QqVS9jyPyNUEQcMVoM7pcHmw9VI9YgwZT0qLlLouITht0S8jYsWNRXFyMr776Cj/96U+xfPlyHDhwAADwX//1X2htbcUnn3yC3bt34+GHH8YNN9yAkpKS8x7vmWeegclk6v1JTU0d+rOhc6RER2DaGDNaO10Yn/jtNLqiKCA73oB5OfFIitLhw/01cI/QN8UPPvgABQUFuO+++xAfH4/c3Fw8/fTT8Hg4cof8Q6tSYMGEBCSZdNhYWouTzRdvBSQi/xv22jHz5s3DmDFj8MgjjyAzMxOlpaWYMGFCn/szMzPxxz/+sd/H99cSkpqayrVjRliXy4N/FldjUW4iTDpVv/sIgoD169fj6quvPue+TY02aEQRM2O+nRZ+1qxZmDRpEl544YU++44bNw6VlZW4+eabce+996KiogL33nsvHnzwQTz++OO+fFpE/SqttuFUSyeuzIpFpIYTRxP5gixrx3i9XjidTnR09HyzEMW+jSsKhQJe7/m/YWs0Gmg0XJRKblqVAtdPTcWeqhY4XV58Lyu2d5jjQKbGXnX4FKqdLtycGIMnM5OhVyrOu6/X60VcXBz+/Oc/Q6FQYOrUqaiursZvfvMbhhAaEbnJJuQkGvFZRSNEAfheZiyngCeSwaBCyKpVq7B48WKkpaXB4XBgzZo1KCoqQmFhIcaNG4fMzEz85Cc/wXPPPQez2Yz3338fmzdvxocffuiv+smHRFHApRkxONHUji2H6uD2SFArRXS5PBddgffa+Gj8rqoe6+tbsb2lDf+aknXefRMTE6FSqaBQfBtUxo8fD6vViu7ubqjVnMuE/E8UBczMtsDe5UJhWR3SzRF9LlkSkf8NKoTU19fj1ltvRW1tLUwmEyZOnIjCwkLMnz8fAPDxxx/j5z//OZYuXYq2tjZkZmbijTfewFVXXeWX4sk/0s2RSDdH9tlWVmMDAByosWOJxwuV4tsWrz9W1ePv1mYAwNsTR2N5yXGsrqo77/GnT5+ONWvWwOv19racHT58GImJiQwgNOKMWhUW5SbgeGM7NpTU4pKMGFgMbJ0lGgmDCiGvvPLKBe/PysriDKkhpq2tDRUVFb23Fe31+MO6TzAtJwOX5mVj1apVeGFPCUyrfgWDQoQAYKHNilfKDyLO4UBDQwOKi4uhVqt7R1H99Kc/xUsvvYSHHnoIDzzwAI4cOYKnn34aDz74oEzPkggYFRuJUbGR2FXZjP2nWjEj29InbBOR7w27Y6qvDaVjC/lPUVERZs+efc72eT+4AZveX4sf//jHKDpYjq5nXwYACACscyafs396ejoqKyt7b+/cuRMrV65EcXExkpOTcccdd+DRRx/tc4mGSC5OtwefHW5EdKQaU9M5pJdoIIby+c0QQkNSZ+9Cg8PZOwur0+uFCAG3lhyDze3BjQkxePTwKTydlYzbUywyV0s0NFZbF/ZVtSA32YTUmAi5yyEKaEP5/GZbIw1JvFGLhrOWT9eIIlSigKVxUdhn74BaFLA8yYzHK2qw29YuY6VEQ5dg0mJxXiJaO1woLLOis5tz2RD5EkMIDVl/jWj/LyEGo3UarDx0ElVd3XBJEu4srYTzAsO0iQJdXooJ88bH48vjTdhxtJGr9BL5CGfpoSETBAGSJPWZX0EUBDyemYQ3qpugEIAFZiNiVEp4+Z5NQU4hCpg9Ng6tHd0oLLMiM86AzDi93GURBTW2hNCQmSPVaOpnhdIFsSb8X/5ovDmx5+eF8WnQnWeUgb9W6SXyl6gINRblJsIrSdhYWgtbp0vukoiCFkMIDZnT7YVONbzRLP5YpZdoJGTHG7BwQgJKq20oKq+Hh819RIPGyzE0JI4uF/acaMGl51l9d6D8sUov0UgRBAHTM2PR7nRj84E6pETrekeMEdHFsSWEhuREUweumZwsdxlEASFSo8Si3AQYtSpsLK1FvaNL7pKIggJDCA2JxaCB1c43WqKzpZkjsCg3EVVNHfjkQB263RwVRnQhvBxDQ9Lt9nJKa6LzuCQjBt1uL7YfbuCsq0QXwE8RGpLUmAicaOrA1kP12FHRKHc5RAFHrRQxLyceKdE6bCipxamWDrlLIgo4DCE0ZItyEzB7XBzijFocbWiTuxyigBRv7Jl1tbm9G5vKrOhycdZVojN4OYaGbYwlElsO1WOMZfATN313ld7jx4+juLgYMTExSEtLw503349jFSewYet70ESoAADFxcW9j+1vlV6iQDQxJQo5iV5sP9KASLUSl42K6TPRH1E44gJ25BOfHqxDwRgzItSDy7XnW6V3+fLl+P3//hHzC5ahyVGH99Z8iLxZKQDQ7xv3d1fpJQpkjW1O7DrejPGJRmTERspdDpFPcBVdko3L48WmsjosmZjos2PuXH8UewtPAADiRxlx3aOX+OzYRIGgrMaGk80duDLLgkgNG6YpuHEVXZKNSiFiTFwkKht9t2LuhBlJEEQBpjgdLGkGnx2XKFBMSDJhQU4Cdp9owedHuDAehR9Gb/IZlUKE6MNr3EazDvf+4dxLNUShRBQFzMy2wNbpwsZSK0Zb9BibwNBN4YEtIeQzp1o6kRytk7sMoqBk0qmwOC8RChH4aH8tbB1cGI9CH1tCyGfcHi8UInv7Ew1HZpwBYyx6fFHRBI8k4crMWIj8u6IQxRBCPsPRhkS+IQgCvpcVizanG5sOWJERG4lxCeyoT6GHl2PIZ7xesGMdkQ/pNUosyk2ESiFiQ0ktWju65S6JyKcYQshnJqVF4YNvajg9NZGPjbHosSg3AQdq7dh2uAFeL8M+hQaGEPKZWL0GyyYlo7TahsY2p9zlEIUUQRAwbUwsLkmPxqYDdThktctdEtGwMYSQzy2ckIAth+rh9nAZcyJfi9QosSg3ofcSja2To2goeLFjKvmcIAiYkGTEe/uqER2hRqxejclpXMqcyJfGWPQYHRuJLyqaAADTM81ci4aCDqdtJ7+rqG+DrbMbU9Nj5C6FKCTZOl3YebQRWfGGIS0kSeQLnLadAlJmnB5GrQpfVDTKXQpRSDLpVFiUmwi3R8LGUis6ut1yl0Q0IAwhNCKy4g1IN0fgo/0cZkjkL2MTDFiQE4+vjzfjy2NNHDJPAY8hhEZMSnQErspLQLnVgU1lVnS5PBd9TEe3m2+kRIMgigJmjY1DVpweG0utONnMIfMUuNgxlUaUIAi4fLQZ3W4vdlU2o6Pbg0i1AhmxkdCqFDjV0oHqlk5o1Qp0dXtgilDB3umGWingyiwLVArmZqKBMOs1WJyXiJJTNhyotWLWWAs0SoXcZRH1wY6pJDun24PKxg50u71IjNIiVq/pbf0409u/y+XBxlIrluYncX0aokFyebzYfrgBURFqTE3nSDXyD3ZMpaCkUSowNsGAvBQTYvUaAD3h4+zhhlqVAotyE/BxSS0vzxANkkohYu74eCRH6bChpBZWW5fcJREBYAihIKJVKTA9MxZFhxvkLoUoKCWYtFicl4jq1k58erAOLk4oSDJjCKGgEhOpRmq0DvtPtcpdClHQmpoejemZsdh6qB4lp2xyl0NhjCGEgk5mnAGNbc4Bja4hov5pVQosmJCAqAgVNpTUoonrPZEMGEIoKGmUCrBrCNHwpcZEYFFuAo7Ut6GovJ4r9NKI4hBdCkpOtwc6NYcbEvmCIAi4YrQZ7U43Nh2wYlSsHmMTDHKXRWGALSEUlLhQF5Hv9azQmwilQuAKvTQi2BJCQaej2w2diq0gRP7CFXpppLAlhILOrsoWTEqNkrsMopAmCAK+lxWLvBQTNpZacayhTe6SKAQxhFBAq27tRJvz2xVBWzu6IaCnZz/5z/bt27F06VIkJSVBEAS8//77F9y/trYWN910E7KzsyGKIlasWDEidZL/mXQqLM5LRLfHyxV6yecYQihgnWzuQHFVK/ZVtWDroXpUNrbjy2NNuDIrVu7SQl57ezvy8/OxevXqAe3vdDphsVjw2GOPIT8/38/VkRzGJRgxPyceXx1vxldcoZd8hH1CKCA1t3ejrMaOJRMTAQCd3R5UNXdgUW6izJWFh8WLF2Px4sUD3j8jIwMvvvgiAODVV1/1V1kkM4UoYPbYODS2ObGx1IrcZBNSYyLkLouCGFtCKCB1np6IrLDMCgDQqRUcMkgUIGJPr9Db2uHCpjIrnG5OHEhDwxBCASk5SodpmWbUtHai3clr0ESBKC/FhNnj4vD5kUbsrWqRuxwKQgwhFLDautyI1CgRqeFVw0DkauyE47NqOLafQmd5s9zlkEzOrNCbaNJiQ0kt6uxcoZcGju/uFLASTVqoFJybINB4uz1wbD0Jx/ZTgCAAbi8ErRLJTxTIXRrJKNGkQ2KeDntOtKCsxoYZWRYoFfyeSxfGEEIBSxAEROnUaO3oRlSEWu5ywprH0Y3O0ka4W51o+6IaHns3DDNTYJydCnvRKXTssspdIgWIqenR6HJ5sOVQPZKidMhNNsldEgUwhhAKaDOzLdh0wMpRMSOsra0NB3fsR/PbhwAA+1//DPotbYjSGTH6knF4vuZdWN+qw5sL3gQASG4vdv59CwS1Am1tbWhoaEBxcTHUajVycnLkfCokgzMr9J5oasfHJbWYNsbMLxLUL4YQCmiiKKDL5UVHtxsRar5cR8ru3bsxe+Hs3tu/2PISAOCHc67Bqz97BfXbG1BVVQUAUJo08Ha4Me3/ze3df8+ePVizZg3S09NRWVk5orVT4Eg3RyItJgI7jzbBK3H6dzqXIAXYjDN2ux0mkwk2mw1Go1HucigAvP11FRZOSEBMJL9JjSTJK6H6Pz6HIkYLRaQK3ScdvfepUvSIuXEsVJaeOSIklwcuawfqVxfD8tN8aNL5t0t92Tpd2FHRiPGJRmTERspdDvnBUD6/GUIo4HW5PPjyWBNmjY2Tu5SwYy86CXdjZ88NCdBNMAOigOa/l0PqdEMz+tvr/e6mTnhs3bDcmw9NGv92qX+l1TZUt3ZiZraFyy+EmKF8frN9mwKeIABHG9oxM1tiU+4IM85K7Xd7zPXZ6NjfAJz1FUZtUENQiVDF81sunV9usgnjEgwoKm+AWa/G5LRouUsiGbElhIJCc3s3vj7ehAlJnCaaKFRUt3bim5OtuGxUDGL1GrnLoWEayuc3B3FTUIiJVGNRbiK2ltfjZHOH3OUQkQ8kR+lwVV4ijta3oai8Hl5vQH0nphHAyzEUVG4tyEDJKRsO19UBACYkmZBg0spcFRENx+WjzWhzulFYZkV2ggFjLHq5S6IRwssxFLQkSUJZjR1WWxfSzRHIiucCd0TB7mCtHSeaOjBrLDuuBhtejqGwIggCcpNNmJcTjy6XF58cqONqnkRBbnyiEfPGx+GLikYUn2yVuxzyM4YQCgl5KSbMyLbgo/217DNCFOSUpxfFsxg0+LikFk1tTrlLIj8ZVAh5+eWXMXHiRBiNRhiNRhQUFGDDhg199tm5cyfmzJmDyMhIGI1GzJgxA52dnT4tmqg/aqWIf5ucjD0nWnCiqV3ucohomJKjdFicm4DDdW3YfrgBAdZ7gHxgUCEkJSUFzz77LPbs2YPdu3djzpw5WLZsGcrKygD0BJBFixZhwYIF+Prrr7Fr1y7cf//9EEU2uNDIEAQBV58OIm6PV+5yiGiYBEFAwRgzJqVFYUOpFccb+QUjlAy7Y2pMTAx+85vf4I477sAVV1yB+fPn45e//OWQj8eOqeQLHd1uvLPrJJZPy+AEZ0QhpKzGhlMtnZg11gKNkh1XA8mIdkz1eDxYu3Yt2tvbUVBQgPr6enz11VeIi4vDtGnTEB8fj5kzZ+Lzzz8f6q8gGrIItRL/NiUFL22pwMnmDs4/QBQiJiSZMGdcHLYfbsT+U61yl0PDNOgQUlJSAr1eD41Gg3vuuQfr169HTk4Ojh07BgB44okncNddd2Hjxo2YMmUK5s6diyNHjpz3eE6nE3a7vc8PkS+YdCrcOzsTXS4P/vlNNT49WCd3SUTkAyqFiPk58YiOUOPjklq0dnTLXRIN0aAvx3R3d6Oqqgo2mw3r1q3DX//6V2zbtg2tra2YPn06Vq1ahaeffrp3/4kTJ2LJkiV45pln+j3eE088gSeffPKc7bwcQ752srkDtk4XcpNNF9+ZiIKCJEnYebQJXgmYnmnm5VcZjcjlGLVajczMTEydOhXPPPMM8vPz8eKLLyIxMREAkJOT02f/8ePHo6qq6rzHW7VqFWw2W+/PyZMnB1sS0YCkxkRw+C5RiBEEAdMyY5GXbMKGUiuqmvg3HkyGPWzF6/XC6XQiIyMDSUlJKC8v73P/4cOHkZ6eft7HazSa3iG/Z36I/KXb40Ule9cThRxThApX5SXC1ulCYZkV3W6OjgsGg1o7ZtWqVVi8eDHS0tLgcDiwZs0aFBUVobCwEIIg4N///d/x+OOPIz8/H5MmTcIbb7yBQ4cOYd26df6qn2hQfpCfhN9uOoyfLchmsy1RCMpLMWFsggFF5fVIitLx8muAG1QIqa+vx6233ora2lqYTCZMnDgRhYWFmD9/PgBgxYoV6OrqwsqVK9Hc3Iz8/Hxs3rwZY8aM8UvxRIMlCAJunZaOTw/Ww6hT4bJRMXKXREQ+plaKWDAhAZWN7fi4pBZXZsXCoFXJXRb1gwvYUdg61dKBAzV2zB0fD4XIVhGiUCRJEj470gitSsEvHX7GBeyIBiElOgLTM2OxqczKfiJEIUoQBMzItiAjNgIf7a9Fnb1L7pLoLAwhFNYiNUoszktETWsnalq5xhFRqIozaLFkYiKqmjuwtbyeExgGCIYQIgCXjzbjGy4bThTyLs2IwaUZMSgss+JYQ5vc5YQ9hhAiAApRgEIUuEonURjQn24B7XR5sLHUCqfbI3dJYYshhOi0SzNi8N7earnLIKIRMiHJhLnj47CtvAGl1Ta5ywlLDCFEp0VHqjE2wYANJbX4oqIRNa2dbBkhCnEqRc9wXoNWiY/218Le5ZK7pLAyqHlCiEJdbrIJuckmdHS7cbK5E2U19XC6PbDoNbh8tFnu8ojIT9LNkUiLicBnRxqhUYr8ex8hnCeEaABOtXSgtNqGhRMSONMqUYhrcDixq7IZU9KikWDSyl1O0OA8IUR+khIdgWmZsVi9tULuUojIzywGDa7KS0R1K4fz+htDCNEAGbUqfH9iEr461iR3KUQ0Aqamx+Cy08N5j3I4r18whBANQkZsJMx6DTaVWUP229H/vb8Rl86YjxhLz6WnVc+/gn99U4ONpbX45EAdGhzOPvvX1tbipptuQnZ2NkRRxIoVK845ZllZGa699lpkZGRAEAS88MILI/NkiIbpzISGTpeXq/P6ATumEg1SZpweyVE6fFxai6np0Ug06eQuadh2VTbjd58eQVVzBw59/RW63GZorrwDWP80XvnsON6u29e7b1pMBLb9+6zevjFOpxMWiwWPPfYYnn/++X6P39HRgdGjR+P666/HypUrR+Q5EflSTpIRmXF6bC2vR1pMBMYnss+iL7BjKtEwfHWsCa2dLhi1KuSlmKDXBFeuf2f3STyybj8AYGy8AbPHxSFWr8b3JybBpFMhQqPE2nfW4aqlP4DbI2HzwTo8sm4//vSjqVg4IeGc482aNQuTJk26YEtHRkYGVqxY0W+LCVEwqKhvw5E6B2aNjYNOrZC7nIAxlM/v4HrHJAowZ4bxdXZ7UHyyFU63By6PBJNOFRQrdv5l+7Hef99waSru+N6oc/bRqBS9y6BfNyUFnx6sw4q1xfjtDflYnMvRQhR+MuP0GBUbiaLyesTqNchPjZK7pKDFPiFEPqBTK1AwxoxZY+MwPyce8UYNioNgLZpNK2dgy89mwqhVost18amrRVHACzdOxsxsC+79v72Y/uwW/O+mctg6OMEThReFKGDu+HjERKo5ydkwsCWEyA/SzZEoq6nFpAD/hiQIAkZb9BBFAe1ON+rsXRDQM0TxfC0cOrUCL98yBV8db8a6Pafw58+O4f3iGlwzJRm2ThdnmaWwkhoTgZRoHT470gidWoFLMwK/BTSQsCWEiKBRivhD0VFc/vSnuOzpT/HCJ0cuuL8gCLhitBnPXZ+PwhUzkB2vx18/O46DtXas31eN324qx4mmdgYSCguCIGBGtgVpMRH4aH8tmtqcF38QAWBLCJFfeL0SNMrgyfj/d+flONnSCQD473+WoqWje8CPTTdH4q/LL4UkSZi0wQBEqvH6F5X4/ZYKZMXp8f8uS8M1k5MRHan2V/lEASHeqMVVeQn48lgzvFYHpo0xs8/URTCEEPnBZxWNQbX2RGacAZlxBgCARuqG9dghFBe7AQDHjx9HcXExYmJikJaWhlWrVqG6uhpvvvlm7+OLi4sBACpvN8bGCLhnUQwONnRgn12PZz4+iGc/LMH307yYPz4enV1OHDtxEsXFxdDr9cjMzBzx50vkL4IgoGCMGS3t3fi4xIop6VEhMYzfXzhEl8jH2p1ufH28GbPHxcldypBc8cDv8NVLD52z3ZA3Dz969Ndo3/w7VFZWoqioqPe+/r7tpaeno7KyEo1tTryw/gs8devcc/aZOXNmn+MQhZo9J5ph73JjZpYFohjarSJD+fxmCCHysS6XBx/tr8XVk5OhCMI3ndJqG0qrbZAAeCUJkgRIAD78pgatHS4UrpwxpONW1Leh3tGFz4404uWio5g2xow1d13h09qJAlGb043thxswIcmIdHOk3OX4DecJIQoAWpUC8yfE48P9NchJNCIr3iB3SYOSm2xCbrLpnO0VdQ58XdnSe7umtRNrd52EKAA3X54Oi0GDLpcHggBolOdO4JQZp0dmnB5T0qJRVN6A0mobVm+twPWXpMCoVUEUBChEISiDG9GF6DVKXJWXiJJTNmw+UIfZYy1QKoKnz5g/sSWEyI9ONLVj66F6pJkjcGlGDAxaFSob2+F0e5Edrw+qTmv//c9SvLnzBKIjVGjv9vSuoaHXKOGVJOg1StQ7nFArRXz2yGzEG8+/BPrG0lo8+a8DqLV19dmuUYrYuGIGRsWG7rdFCm9dLg+KyhswxhIZdF9QLoaXY4gClCRJ2HG0CY4uF0Zb9NCpFDhYa4dKIeKK0eagmPr5YK0dH5fUQqdWIEKlQIRGiSlp0TBHqvGXz47hD0VH++xf8sSC3plWz2fH0UY0OJzwShLq7E48u+EQ3vlJQVDMNks0HIfrHDjW0I7Z4yz9thwGI4YQoiDj8UrYVGbF4rxEuUsZtub2brR1uTHjN1sBALF6NdbcdQWyB/ht72hDG+b+dhtDCIUNl8eLovIGJJq0/V4CDTYMIURBaGt5PWaPDc6RNP1xe7w4ZHXgvjV7oRAFvLr8UmQM4PLKiaZ2zPxNEQBAEACFIEAUBAgCMCo2Ev964HtQ8To6haDKxnYcrLVjRrYFkUG2CObZ2DGViGSnVIjITTbhzz+6BPe8tQdLfvcZxsTpAQC9PWBO94UR+t7sJUmAW5LQMy4HOGR1YPvhBswdH+/3+olGWkZsJNJiIrDtSANMOhWmpEXLXdKIYQghIr8Ym2DAvx74Hv607Sia2rvxbZtrzz8kCb3bpNPbMi36s/Y4vQ8ktHW5kRYTMXLFE40wURQwe2wcalo78dH+WkzPNCMqIvRnGWYIIZJZgF0R9Sm9RomfLRgrdxlEQSMpSodEkxZfVDRBqehZoymU8QIrkYzanW5oQ6RnPBH5hiAI+F5WLLxeCW1Ot9zl+BVDCJGMdlU2Y0p6+Fz/JaKBc3q8iFCF9pcUhhAiGbU53dCG+JsMEQ2NWiHC7Q3dy7UAQwiRbLrdXjS1daOsxiZ3KUQUgLo9XihDfBkDhhAimaiVIpZPy0C93Ym9VS0XfwARhRWVKKKlo1vuMvyKIYRIZrPHxaGlPbTfaIho8KZnmnG0oR2bD9TBG6KXZRhCiIiIApAgCLhsVAwKxpjxxdFGucvxC4YQIpk1OJyIjgz9SYmIaGj0GiW6XF443R65S/E5hhAimX3wTQ0mpUTJXQYRBbDZYy349GA9Kurb5C7FpxhCiGR29aQkbCyzyl0GEQUwpULEVXmJ8HglbCy1ojVEOqwyhBDJzKzXIC0mAkcbQusbDhH53tgEAxZOiMfuyhYcC4H3DIYQogBgtXVh9ACWuyciEgQB83LicSQELs0whBDJrKPbDZ1aAeG769kTEV2AQaPEyeYOucsYFoYQIpltP9yAy0fFyF0GEQWZaZmx2HOiBS6PV+5ShowhhEhGnd0eNLR1Q6ngnyIRDZ7D6Ya90yV3GUPGdz4iGXV7vPB4vPjkQB2KyutxpM4hd0l0Edu3b8fSpUuRlJQEQRDw/vvvX3D/9957D/Pnz4fFYoHRaERBQQEKCwtHplgKebdcnoaSahsqG9vlLmVIGEKIZGTSqXDb9FGYPS4Os8bGQa0U8cmBOmw5VIfO7tCbmCgUtLe3Iz8/H6tXrx7Q/tu3b8f8+fPx8ccfY8+ePZg9ezaWLl2Kffv2+blSCgeCIGDW2DgcqLUH5dTugiRJAVW13W6HyWSCzWaD0WiUuxwiWXi8Ej4qqcXSiYnssBrABEHA+vXrcfXVVw/qcRMmTMCNN96I//7v//ZPYRR29pxoQaZFD1OESrYahvL5rfRzTUQ0BApRwIysWGw+UIcFExL63aelvRu2Thc6XR50ujzocnnQ7fb2hhZJkhAdocaEJCMUogC3V4KKfU9k5/V64XA4EBPDzsjkO/Yul6wBZKgYQogCVFSEGhKAwjIrNEoRkgSIooAzjZdREWpER6gQE6mGVqWATqWASiH0aTlpbHNi57EmeCVAKQpwuj3weHv+bTFokBEbCb2GbwO+VF9fiIqKZ6HVJUOljEJU9OVQKY1QKPUQBAV+//u/w+Foxfz5GWhq+gwSPIAkQaWKgsk0We7yKUjFRKix5VAdBEFAarQOmXEGuUsaEL77EAWwhedpBRmoWL0GV2ZZztkuSRKa2rtxsNYOR5cLoiBgbIIBbV1uVLd2QgIQb9BifKKhN9R8dqQBntPXnM9cwxUFAZekRyMyBIOM2+2A3V6Crq5TSEj4N4hi32+ZZ8Jgc/NOeL1Leu9vaChEZ1cVOruqAAD1DRt6H/Ppp214/n8b8OQv4nGq+iGcqu77O8dmP4mUlFv8+KwoVOWnRvX++0idAx/ur8GSvMC/nBt67xxEdFGCICBWr0GsXgMAsHW6sPNoE061dCAzTg8BwDenWvHV8SZkxEbC65WQHW9AakxEn+N4vBK+OtaEjm4PoiJUyE+NCopLPh5PB05UvQKP2wG32wGPpwMKRQQyMu6F221H+eEnYLPt7d3/4KFVMBrzYTRMRHXNWsTFLUJnZ0/IOHXqDWwtWoeE+KsRa5kLj7cTAHDJJe9BH5kFp7MOgIh/vLcJLzz/INaseR2LFs0CIEAQFAAEOJ1W7N5zHcoPP46kpBsgilxVmYYuK96AeJMWW8vr4fZImJFtgValkLusfjGEEBEi1Qq4vV7ceeXo3m2SJKGyqQMnmtqhEAUcbWhDudUBpULAhCQTLAYNFKKAGL0aXa2dsNq7IJ1sxaUZgd/XobT0ITQ2bYFOlwGVygRR1KC19WvU1L4DAFAo9MhI/ymioq9AcfFyAIDDcRButwOS5EJ9fSGioy7tPZ4oalFX/xGsde8DAIyGiTDox0MU1YiIGIW3334bd9/1INauXYtly5adU49Wm4iJeS9jf8lP4XLZoNGc23pFNBhGrQpzxsXD7fHi00P1UCtEjLHokWaOuPiDRxBDCBFBqRBhjtRg84E65CQZkRylgyAIGBUbiVHfWdPG65Ww72QLSqttgABolQrMGRffZ58zl25mZFkgioHXHNzYtAUA4HK1wO12wOVqAgBERmZh9KgViI2d09saMXfO0T6PbWtrQ0VFxelbf4PB8Ciio2bDaNTCZGrA00+/hYaGDrz5Zs/j16xZg+XLl+PFF1/E5ZdfDqu1Z8VknU4Hk8nUe1y1OvZ0Tc0MIeQzSoXYe1n3QI0dXx1rwuWjzTJX9S0O0SWiXo1tThyosWNG9uA/BL1eCdWtnfjmVCsyzD3h5ctjTeh2ezF7XFxANQe3t1eguubvUKtjIXldOHb8+d77RFGLvNzfw2bbC7enDfHxS6FSRiMiIgOCIKKoqAizZ88+55jLly/H66+/jttuuw2VlZUoKioCAMyaNQvbtm077/5ndHQcx84v52HK5DWIjr7c58+ZCADKamxwur2Ykhbt82MP5fObIYSI+thX1YLGtm7kJZsQb9Sc07HN7fGitMaOensX1Mpv+3+IgoB4oxZjE/r2yvd4JWw/0oButxcRagUuH2Xu87hA0NhUhG++ueOC+2Rl/ieSkq6HKGrP6aTqC2dCSH7+K4g1z/L58YnO2HKo7pzWS19gCCEin5AkCWU1dnxR0YjseAPOziGSBOSlmHo7tQ5Gl8uDovIGLMod3qgff/F4OtHS8iVEUQ2DIQdtbUfgdFpRdmBl7z4x0d/D5Mlv+Px3V1W9gqPHnsOV3/saSmVwDK+k4FLT2oldlc3Qa5SYOz4wQgj7hBDROQRBQG6yCbnJpovvPAhalQIRagXane6AHNarUOgQG/vtpZbo6MsAABptIvbt+xEkyYXmls/xxY6ZiIzMRFrq7YiOvuL0KBec7rj67XT7oqiBQqEb0O+uq/8Y5piZDCDkc5IkYcfRJihFAcsmJctdTh+B9y5ARCHte5mxKCyzYnFeotylDFh01KWYM/sQurpq0dD4CZxOK5qatmNf8a0AAItlATo7T6Gt7cA5j50yZS0c9v2w1n0AhaiD0ZQPnS4dSYk3wOvtAgA4nVbY7cWYkPP8OY8nGg6PV8L7+6oxe1wcYiIDb+g3L8cQ0Yg70dSOpvZuv3SOGymS5EVd/Uc4evQ3EEUNAAFKRSTS0+8BALS3H8ax4y8M+HiiqDl9KUbvn4IprOw42ohutxeSBFw2KmZEWh7ZJ4SIgsY3J1tR2dSOlOgITE0P3jByPpIkwW7fh+bmHYjUZ0Gt6hkWqdePxfbPLoUkuRAfvxSW2HkAAK0uFSZjvpwlU5Brc7qx90QL2p1uXDoqZkj9toZjKJ/fg+qi/vLLL2PixIkwGo0wGo0oKCjAhg0bztlPkiQsXrwYgiDg/fffH8yvIKIwkZ8ahWWTkpEao0NhmRXFJ1vh9njh8nhHtI7t27dj6dKlSEpKGtB7Vm1tLW666SZkZ2dDFEWsWLGi3/0EQYDJNAWjRt2POMtCREVdgqioS6BUGjBn9iHMnXMUuRNeQHz89xEf/30GEBoSp9uDTw/WYWt5PfZVtaBgjBmL8xJHPIAM1aDaZ1JSUvDss88iKysLkiThjTfewLJly7Bv3z5MmDChd78XXngh4OerJ6LAEGfQYuGEBDQ4nNhxtAmCALg9PQ20iVFajI03+PX9pL29Hfn5+bj99ttxzTXXXHR/p9MJi8WCxx57DM8/zz4cJK+th+oxb3w8lEGwXEJ/BhVCli5d2uf2U089hZdffhlffvllbwgpLi7Gb3/7W+zevRuJicHT8YyI5GUxaGAx9J0k7VRLB4rKGwAAgtCzz6jYSESofXd9e/HixVi8ePGA98/IyMCLL74IAHj11Vd9VgfRUGiUiqANIMAwRsd4PB68++67aG9vR0FBAQCgo6MDN910E1avXo2EhMCcB4CIgkdKdARSonvWupAkCQ0OJ0pO2dDh8kCSJGhVChSMNqO5vRv7q204u73E5ZGgVoqQJAmCIMArSRDQM88JAFyZFRvUb95Eji4Xukf48qWvDTqElJSUoKCgAF1dXdDr9Vi/fj1ycnIAACtXrsS0adP6XaDpfJxOJ5xOZ+9tu90+2JKIKAwIgoA4oxZxRm3vNlunCx+V1MKoVWFWtmXAl226XB58VFKL2ePiYNT6fvZTIn9rcDjx2hfH8fD8bLlLGZZBh5CxY8eiuLgYNpsN69atw/Lly7Ft2zZUVFRgy5Yt2Ldv36CO98wzz+DJJ58cbBlERDDpVPj+xKRBP06rUuAH+Ul4v7gaV09KZh82ChqSJOHLY81we714ZNE4ucsZtkGHELVajczMTADA1KlTsWvXLrz44ovQ6XQ4evQooqKi+ux/7bXX4sorr+xdzOm7Vq1ahYcffrj3tt1uR2pq6mDLIiIaFEEQMHd8PLYcqvfLFNZEviZJEv6xtxrzc+Jh0oVGC96we3d5vV44nU48+eSTuPPOO/vcl5eXh+eff/6cDq1n02g00GiCYygREYUWo1aFjNhIlFbbfD5FPZE/qBQC6u1d4RlCVq1ahcWLFyMtLQ0OhwNr1qxBUVERCgsLkZCQ0G9n1LS0NIwaNcpnBRMR+VK8Dnj78z2wJ/dMrnT8+HEUFxcjJiYGaWlp+NmKh3DsyBGs++cHUCh73jKLi4sBAG1tbWhoaEBxcTHUanVv/zgifxAEAQpRQEe35+I7B4lBhZD6+nrceuutqK2thclkwsSJE1FYWIj58+f7qz4iIr/avXs37r5mbu/tM5eHly9fjtdffx27tnyCaqsV2//vNcxefhcAYPLkyb3779mzB2vWrEF6ejoqKytHtHYKPyqFOOIT+vnToELIK6+8MqiDB9iM8ERE55g1axYkScKWQ3WYM+7cviFr163Dp6/8ERn5U3q38b2N5CJJEi7JiJG7DJ/hKrpEFPZa2rvR2uHq976k7PH40a9fHOGKiM5H6J37JhRwph4iCnufHKzDv01OlrsMoosqGG3Gl8ea5S7DZ9gSQkRhLzlahyP1bYjUKBGrV0OjVJx337/vqkKcUQuLXgOzXo1Ek24EK6VwZ4pQodPllrsMn2EIIaKwN21MLCob22HvdOGw1QGvJCHdHIFRsXocrLWjwdEzq7MgADWtXSi3tmHhhJ7+I9+ctGFSahQSTNoL/Qoin7F3ukPmkgxDCBERgIzYSADA+MSeobonmtrxeUUjxicY+swhMmtsXJ/H5SabsKOiEfWOLkxMiRqxeik8fXmsCRFqRUgEEIB9QoiI+pVujsTMbEuftWrOZ1pmLEw6FT7aX4umNudF9ycaqo2lVlyZZbn4jkGCLSFERD6Qbo5EWkwE9la1YG9VK8YlGJAaEyF3WRRiJqaYoFOfv89SsGEIISLyEUEQMDW9Zw6H/adaccjqgFGrRG6yCZEavt3S8HS5PFCIoXEZ5gxejiEi8oOJKVGYnxOP3GQTSqpt+PRgHcqtDrnL8ovt27dj6dKlSEpKgiAIeP/99y+4f21tLW666SZkZ2dDFEWsWLHinH1ef/11CILQ50erDe/Ov5VN7SHX74ghhIjIjyI1Slwx2oy54+Ph8nhRWGbFyeYOucvyqfb2duTn52P16tUD2t/pdMJiseCxxx5Dfn7+efczGo2ora3t/Tlx4oSvSg5Knd0eHKkLrSDL9kEiohGSm2xCbrIJxSdbcbK5A9MyY+UuyScWL16MxYsXD3j/jIwMvPhizyy0r7766nn3EwSh34VRw43HK+GTg3UYl2DA5LRoucvxKbaEEBGNsEmpUchOMOCfxdXocoXOiqi+1tbWhvT0dKSmpmLZsmUoKyuTu6QR53R78ME31bh8VAzSzZFyl+NzDCFERDKI1WuwODcRG0prsf1wAw6HWDP7cI0dOxavvvoq/vnPf+Ktt96C1+vFtGnTcOrUKblLG1GFZXVYOjEJURFquUvxC0EKsOUg7XY7TCYTbDYbjEaj3OUQEfmd2+PtmYm1zoHRlkiMsejlLmnAap98Em1bi4DTHyXZ27dh9fgczDebAQASTn/EnPmkOfORc/q/PyorxbjISDyWmQUoFBBEEVAqIIgKQJKgnzkD8atWweVyYfz48fjhD3+IX/7ylyP4DEfemdlQy60OuDzePpPlBbKhfH6zTwgRkcyUChFp5gikmSOwr6oFpdW2oPngsf3zA6hTU2GYN69nw/ZtiJw+HVGTTnc4PT2zZ+8Mn70zfQqAIED5299Cm5KMqGuvBTweSB4v4PVAcnvQ/NprcHg8iF+1CiqVCpMnT0ZFRcXIPsERdMhqx7GGduhUPfOAJEfrguZ1MFQMIUREAWRyWjQO1NjxyYE6JEXpkJMU2C3CUkcHnOXlAID4Rx8BAJiWXAXL1VcP6PGqt9dAN3EiLPfdd859gkoF+0cfAQA8Hg9KSkpw1VVX+abwAJQVZ8CRujbMHhd38Z1DBPuEEBEFmJwkI+blxEOvUWJjaS1OtQTukN6IgivQ7vWi+JtvUHjTzQCA3f/5n9jx17+iqqoKALBq1SrceuutfR5XXFyM4uJitLW1oaGhAcXFxThw4EDv/b/4xS9QVHEEJ9rasHfvXtxyyy04ceIE7rzzzpF7ciNMIQqIjlDD6Q6fzsrsE0JEFOD2VbWgs9sTsEN6t27Zgjlz556zffny5Xj99ddx2223obKyEkVFRb339bcAW3p6OiorKwEAK1euxLo330RdSwti4uIwdepU/OpXv8LkyZP99TRk19LejR1Hm7BkYqLcpQzJUD6/GUKIiIJAra0TB2vtmDMuXu5SLuro978P/fTpiF+1aljHafzLX9D811eQ/dWXPqossG0oqUVytA7jE41QKYLvQsVQPr+D71kSEYWhRJMOuUkmbD5QJ3cpFyWqNegs/qb3tr1wE44tXYqqO+6Et6trwMcRlCpIbrc/SgxIi/MSkRSlw7u7T6GlvVvuckYEO6YSEQWJOKMWk9MEbCytxcIJCf1e0ggE3VVV8La1ofa//guCSoWWNW8DAJxHKlA+aTJiH3wAsffc0zMc9wIEpRKSJ3z6RwA988cAgF4bHh/PbAkhIgoisXoNLs2IwYf7axFgV9N7me+8AwDQ+u663gCS8seXEf9fj0E/by4af/d7HL/mWrRt23bB5yAoFWEXQgBgbIIeHc7weN7hEbWIiEKIWa/BjGwLPvimBotzE6FWBtb3SfNdd8GwcCEUBgPcTU1Qp6dDPL0CbszNN6Njzx7UP/88Tv7kHugumQrD7DlQJSdBlZgIVXIyFGZzTyuPQgGE0eWYM9qdHujUCrnLGBEMIUREQcikU+GqvER8tL8WP8hPgigGzqUZQaGAZtQoAIAy9twRPRFTpyL9b39D+2efofHlP6Jh9WpIHd8OQ1ZERUGTnQ3J6wEkCZLXe9FLN6Gky+UJuGDpLwwhRERBSqUQMT8nHp8eqsf8nMAfNXM2QRCgnzED+hkzIEkSvHY7XLW16K6qgvPIETiPVMB55Ag0WZlylzqiSqttyIwLnmn7h4shhIgoiEVqlEg0aXGqpQMp0RFylzMkgiBAYTJBYTJBO24csGCB3CXJ5mhDG67MsshdxogJj/YeIqIQlptsQlmNXe4yyAeW5CVi++EGeLyB2enY19gSQkQUAtJiInC8sR2jYiPlLoWGQakQcWVWLP6x9xQ0ShFZcQYYtEpUNLRBAKBRKnDZqBgoAqgP0HAwhBARhYDxiUYUllkZQkKAWa/BDZekAgCqWzvR2e3GjCwLFKKAjm43NpVZsTgvOKd2/y5ejiEiChEWgyZsZtoMF8lROmTGGXpbPiLUSsQZtdhzohkuj1fm6oaPIYSIKERMTDahpNomdxnkZ1PSouDocsNqG/gU+IGKIYSIKEQoFWLYdGgMZ4IgoN3pQbxRK3cpw8YQQkQUQiQwhISDBRPi8XlFg9xlDBtDCBFRCAnQ5WTIx9weCRHq4B9bwhBCRBQiisrrYTFo5C6D/EySJGw73BASM6sGf4wiIiIAgE6lgFYVHgufhavKxnYcsjowLdMMo1YldznDxpYQIqIQcfloM042d1x8Rwpah6x2LMpNCIkAArAlhIgopDS2OeUugYahs9sDrUqEIPSdEbXB4cTuymYoFaHVdsAQQkQUQkJh2GY48XgltHW54XC60NLuQmVTO/RaJURBgCRJcHkkKMSe6dpDZZbUszGEEBGFEFEQ4PJ4oVKIkCTpnG/U5Bter4T2bjccXW60OXv+2+50wzvI4UmiIECvVcKoVSLOqEFeislPFQcmhhAiohAyOS0Kv/v0CCanRUGAAKfbi/k58SGz4JkvuDxeOLrcsHe6YO9ywd7phtvr7Q1s0ukgIZxujejv3wKASI0CBq0Keo0SCSYt9GolRJ7nQWEIISIKIQatCivnZfd+GHa5PPjkYB0WTkiQuTLf6S9EeAbRAqEUBRi1Khh1SqRGR0CfqIQqxPpaBAuGECKiEHP2t3GtSgGjVoU2pxt6TWC85TNE0BmB8YokIiK/GZ9owP5TrZg2JtZnx3R7vLB1unp/HF3u3gnjL3Q5QxAEhgjqxRBCRBTioiLUcLrOXfbd65Xg6HL3CRMeSRpQnwiFIMCkU8GkU2F0rB4GLftD0OAxhBARhQGNSkRReX2f0TICAINWCZNOheRoHcYnGkJuHgoKbAwhRERhwJeXYoh8hZGXiIiIZMEQQkRERLJgCCEiIiJZMIQQERGRLBhCiIiISBYMIURERCQLhhAiIiKSBUMIERERyYIhhIiIiGTBEEJERESyYAghIiIiWTCEEBERkSwYQoiIiEgWDCFEREQkC6XcBXyXJEkAALvdLnMlRERENFBnPrfPfI4PRMCFEIfDAQBITU2VuRIiIiIaLIfDAZPJNKB9BWkwkWUEeL1e1NTUwGAwQBAEucvpl91uR2pqKk6ePAmj0Sh3OSGH59e/eH79h+fWv3h+/Wu451eSJDgcDiQlJUEUB9bbI+BaQkRRREpKitxlDIjRaOQfgh/x/PoXz6//8Nz6F8+vfw3n/A60BeQMdkwlIiIiWTCEEBERkSwYQoZAo9Hg8ccfh0ajkbuUkMTz6188v/7Dc+tfPL/+Jcf5DbiOqURERBQe2BJCREREsmAIISIiIlkwhBAREZEsGEKIiIhIFgwhF/HUU09h2rRpiIiIQFRU1Dn3f/PNN/jhD3+I1NRU6HQ6jB8/Hi+++OJ5j/fFF19AqVRi0qRJ/is6SPji3L733nuYP38+LBYLjEYjCgoKUFhYOELPILD56rVbVFSEKVOmQKPRIDMzE6+//rr/iw8CFzu/APDggw9i6tSp0Gg05/2bLywsxBVXXAGDwQCLxYJrr70WlZWVfqs7GPjq3EqShOeeew7Z2dnQaDRITk7GU0895b/Cg4Svzu8ZFRUVMBgM5z3WhTCEXER3dzeuv/56/PSnP+33/j179iAuLg5vvfUWysrK8J//+Z9YtWoVXnrppXP2bW1txa233oq5c+f6u+yg4Itzu337dsyfPx8ff/wx9uzZg9mzZ2Pp0qXYt2/fSD2NgOWL83v8+HEsWbIEs2fPRnFxMVasWIE777yTQQ8XP79n3H777bjxxhv7ve/48eNYtmwZ5syZg+LiYhQWFqKxsRHXXHONP0oOGr44twDw0EMP4a9//Suee+45HDp0CB988AEuu+wyX5cbdHx1fgHA5XLhhz/8Ia688sqhFSPRgLz22muSyWQa0L733nuvNHv27HO233jjjdJjjz0mPf7441J+fr5vCwxivji3Z8vJyZGefPJJH1QWGoZzfh955BFpwoQJffa58cYbpYULF/qyxKA2kPN7vr/5d999V1IqlZLH4+nd9sEHH0iCIEjd3d0+rjT4DOfcHjhwQFIqldKhQ4f8U1wIGM75PeORRx6RbrnllkG9z5yNLSF+YLPZEBMT02fba6+9hmPHjuHxxx+XqarQ0N+5PZvX64XD4bjgPnR+3z2/O3fuxLx58/rss3DhQuzcuXOkSwtJU6dOhSiKeO211+DxeGCz2fC3v/0N8+bNg0qlkru8oPavf/0Lo0ePxocffohRo0YhIyMDd955J5qbm+UuLWRs2bIF7777LlavXj3kYwTcAnbBbseOHfj73/+Ojz76qHfbkSNH8POf/xyfffYZlEqe8qHq79x+13PPPYe2tjbccMMNI1hZaOjv/FqtVsTHx/fZLz4+Hna7HZ2dndDpdCNdZkgZNWoUNm3ahBtuuAE/+clP4PF4UFBQgI8//lju0oLesWPHcOLECbz77rt488034fF4sHLlSlx33XXYsmWL3OUFvaamJtx222146623hrWYYFi2hPz85z+HIAgX/Dl06NCgj1taWoply5bh8ccfx4IFCwAAHo8HN910E5588klkZ2f7+qkEnJE8t9+1Zs0aPPnkk3jnnXcQFxc33KcSkOQ8v+HAX+f3fKxWK+666y4sX74cu3btwrZt26BWq3HddddBCrHJrEf63Hq9XjidTrz55pu48sorMWvWLLzyyivYunUrysvLffZ7AsVIn9+77roLN910E2bMmDGs44Tl1/Kf/exnuO222y64z+jRowd1zAMHDmDu3Lm4++678dhjj/Vudzgc2L17N/bt24f7778fQM8fhyRJUCqV2LRpE+bMmTPo5xCoRvLcnm3t2rW488478e67755z+SCUjPT5TUhIQF1dXZ9tdXV1MBqNIdkK4o/zeyGrV6+GyWTC//zP//Rue+utt5CamoqvvvoKV1xxhc9+l9xG+twmJiZCqVT2+fI3fvx4AEBVVRXGjh3rs98VCEb6/G7ZsgUffPABnnvuOQA9I5G8Xi+USiX+/Oc/4/bbbx/QccIyhFgsFlgsFp8dr6ysDHPmzMHy5cvPGf5lNBpRUlLSZ9sf/vAHbNmyBevWrcOoUaN8VkcgGMlze8bbb7+N22+/HWvXrsWSJUt89rsD0Uif3/4uDWzevBkFBQU+qyGQ+Pr8XkxHRwdEsW+DtEKhANDzZSWUjPS5nT59OtxuN44ePYoxY8YAAA4fPgwASE9PH7E6RspIn9+dO3fC4/H03v7nP/+JX//619ixYweSk5MHfJywDCGDUVVVhebmZlRVVcHj8aC4uBgAkJmZCb1ej9LSUsyZMwcLFy7Eww8/DKvVCqDnjcRisUAUReTm5vY5ZlxcHLRa7Tnbw81wzy3Qcwlm+fLlePHFF3H55Zf37qPT6WAymWR5XoHCF+f3nnvuwUsvvYRHHnkEt99+O7Zs2YJ33nnngv1ywsXFzi/QM39CW1sbrFYrOjs7e/fJycmBWq3GkiVL8Pzzz+MXv/gFfvjDH8LhcOA//uM/kJ6ejsmTJ8v0zOTni3M7b948TJkyBbfffjteeOEFeL1e3HfffZg/f35YXBq/EF+c3zOtSmfs3r2738+7ixr0eJows3z5cgnAOT9bt26VJKln+FJ/96enp5/3mByi28MX53bmzJn97rN8+XJZnlMg8dVrd+vWrdKkSZMktVotjR49WnrttddG/LkEooudX0k6/+vz+PHjvfu8/fbb0uTJk6XIyEjJYrFIP/jBD6SDBw+O/BMKIL46t9XV1dI111wj6fV6KT4+XrrtttukpqamkX9CAcZX5/dsQx2iK0hSiPV+IiIioqAQlqNjiIiISH4MIURERCQLhhAiIiKSBUMIERERyYIhhIiIiGTBEEJERESyYAghIiIiWTCEEBERkSwYQoiIiEgWDCFEREQkC4YQIiIikgVDCBEREcni/wMm8T8JzAod7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.1\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.90\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h2K07uw+JpPMChJubm8rLy2t8jJdfflkLFy7UggUL1LlzZwUEBGjMmDEqKSmp9nt+fn4X3Xd1teXn58vDw0MpKSl2IUeSmjRpUuP66woBBgCAS3TFFVfo119/tX3Oy8tTenp6rfezfft2tWzZUpKUm5urAwcOqEOHDpKkr7/+Wnfffbf+/Oc/S5LKy8t14MABdezY0fZ9b29vlZWV2e2zS5cuWrFihUpLS6u9C3Mh119/vcrKypSTk6M+ffpU2aZDhw7asWPHeedSH+jECwDAJbrtttv097//XV9++aX27t2rkSNHnne3oiZmzJihjRs36vvvv9eDDz6oFi1a6J577pEktWnTRhs2bNC2bdu0f/9+Pfroo8rOzrb7/tVXX60dO3boyJEjOnbsmMrLy5WYmKi8vDwNGzZM33zzjQ4ePKi///3vSktLq1FNbdu2VVxcnEaMGKE1a9YoPT1dO3fu1Jw5c/Txxx9Lkp588kmtX79e8+bN08GDB/Xaa6/Vy+MjiTswAABXd+yAyx5n0qRJSk9P11133aWgoCDNnDnzku7AvPjii3rqqad08OBBdevWTf/617/k7e0tSZo8ebIOHz6s2NhY+fv7Kz4+Xvfcc49Onjxp+/4zzzyjkSNHqmPHjioqKlJ6erquvvpqbdq0SePHj1ffvn3l4eGhbt26qXfv3jWua9myZZo1a5bGjRunzMxMtWjRQjfddJPuuusuSdJNN92kN998U9OmTdPUqVMVExOjyZMna+bMmbW+BrXlZpz78K6ByMvLU1BQkE6ePKnAwEBnl4PG4pdUaWlfKX5LxbgSl9oGaGROnz6t9PR0tWrVSr6+vhUrG8FIvJs3b1a/fv2Um5sri8VSL8d0BVX+ff9/Nf395g4MAMA1WaIqwgRzIaEKBBgAgOuyRBEoUCUCDAAATnLrrbee9xo2aoa3kAAAgOkQYAAAgOkQYAAALoPHKY2DI/6eCTAAAKerHPztYsPjo2EoLKx4Nf5SRgiuRCdeAIDTeXp6yt/fX7/99pu8vLzk7s7/XzdEhmGosLBQOTk5slgslzRqcSUCDADA6dzc3BQeHq709HT99NNPzi4HdcxisSgsLOyy9kGAAQC4BG9vb7Vp04bHSA2cl5fXZd15qUSAAQC4DHd39/OGlgeqwkNGAABgOgQYAABgOgQYAABgOgQYAABgOgQYAABgOgQYAABgOgQYAABgOrUOMFu3btXgwYMVEREhNzc3rV271rattLRUEyZMUOfOnRUQEKCIiAiNGDFCv/zyi90+Tpw4obi4OAUGBspisWjUqFHKz8+3a7Nnzx716dNHvr6+ioqK0ty5cy/tDAEAQINT6wBTUFCgrl27Kikp6bxthYWF2r17t6ZMmaLdu3drzZo1SktL0+9//3u7dnFxcdq3b582bNigdevWaevWrYqPj7dtz8vL05133qmrrrpKKSkpevnll/X8889r6dKll3CKAACgoan1SLwDBgzQgAEDqtwWFBSkDRs22K177bXX1LNnTx09elQtW7bU/v37tX79eu3atUs9evSQJC1evFgDBw7UvHnzFBERoZUrV6qkpERvv/22vL291alTJ6Wmpmr+/Pl2QQcAADROdd4H5uTJk3Jzc5PFYpEkJScny2Kx2MKLJMXExMjd3V07duywtbnlllvk7e1taxMbG6u0tDTl5uZWeZzi4mLl5eXZLQAAoGGq0wBz+vRpTZgwQffff78CAwMlSVlZWQoJCbFr5+npqeDgYGVlZdnahIaG2rWp/FzZ5lxz5sxRUFCQbYmKinL06QAAABdRZwGmtLRU9913nwzD0BtvvFFXh7GZNGmSTp48aVsyMjLq/JgAAMA56mQ26srw8tNPP2nTpk22uy+SFBYWppycHLv2Z86c0YkTJxQWFmZrk52dbdem8nNlm3P5+PjIx8fHkacBAABclMPvwFSGl4MHD+rzzz9X8+bN7bZHR0fLarUqJSXFtm7Tpk0qLy9Xr169bG22bt2q0tJSW5sNGzaoXbt2atasmaNLBgAAJlPrOzD5+fk6dOiQ7XN6erpSU1MVHBys8PBw/fGPf9Tu3bu1bt06lZWV2fqsBAcHy9vbWx06dFD//v01evRoLVmyRKWlpUpMTNSwYcMUEREhSXrggQc0ffp0jRo1ShMmTND333+vhQsX6tVXX3XQaQMu4NiB6rf7N5cs9OUCgKq4GYZh1OYLmzdvVr9+/c5bP3LkSD3//PNq1apVld/74osvdOutt0qqGMguMTFR//rXv+Tu7q4hQ4Zo0aJFatKkia39nj17lJCQoF27dqlFixZ64oknNGHChBrXmZeXp6CgIJ08edLuERZQp35JlZb2leK3SBHdqm5jzZCSekqlhdXvy8tfSthJiAHQqNT097vWAcYsCDBwipoEGKkixBQev/D2YwekNaMvvh8AaGBq+vtdJ514AVyEJYo7KwBwGZjMEQAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmA4BBgAAmI6nswsAIGVai5RbUKJmAd6KtPg5uxwAcHkEGMDJMq1Finlli4pKy+Tn5aHPx/VVpLOLAgAXxyMkwMlyC0pUVFqmJ25rraLSMuUWlDi7JABweQQYwEXw6AgAao4AAwAATIcAAwAATIcAAwAATKfWAWbr1q0aPHiwIiIi5ObmprVr19ptNwxDU6dOVXh4uPz8/BQTE6ODBw/atTlx4oTi4uIUGBgoi8WiUaNGKT8/367Nnj171KdPH/n6+ioqKkpz586t/dkBAIAGqdYBpqCgQF27dlVSUlKV2+fOnatFixZpyZIl2rFjhwICAhQbG6vTp0/b2sTFxWnfvn3asGGD1q1bp61btyo+Pt62PS8vT3feeaeuuuoqpaSk6OWXX9bzzz+vpUuXXsIpAgCAhqbW48AMGDBAAwYMqHKbYRhasGCBJk+erLvvvluS9M477yg0NFRr167VsGHDtH//fq1fv167du1Sjx49JEmLFy/WwIEDNW/ePEVERGjlypUqKSnR22+/LW9vb3Xq1EmpqamaP3++XdABAACNk0P7wKSnpysrK0sxMTG2dUFBQerVq5eSk5MlScnJybJYLLbwIkkxMTFyd3fXjh07bG1uueUWeXt729rExsYqLS1Nubm5VR67uLhYeXl5dgsAAGiYHBpgsrKyJEmhoaF260NDQ23bsrKyFBISYrfd09NTwcHBdm2q2sfZxzjXnDlzFBQUZFuioqIu/4QAAIBLajBvIU2aNEknT560LRkZGc4uCQAA1BGHBpiwsDBJUnZ2tt367Oxs27awsDDl5OTYbT9z5oxOnDhh16aqfZx9jHP5+PgoMDDQbgEAAA2TQwNMq1atFBYWpo0bN9rW5eXlaceOHYqOjpYkRUdHy2q1KiUlxdZm06ZNKi8vV69evWxttm7dqtLSUlubDRs2qF27dmrWrJkjSwYAACZU6wCTn5+v1NRUpaamSqrouJuamqqjR4/Kzc1NY8aM0axZs/TRRx9p7969GjFihCIiInTPPfdIkjp06KD+/ftr9OjR2rlzp77++mslJiZq2LBhioiIkCQ98MAD8vb21qhRo7Rv3z6tXr1aCxcu1NixYx124gAAwLxq/Rr1N998o379+tk+V4aKkSNHavny5Xr22WdVUFCg+Ph4Wa1W3XzzzVq/fr18fX1t31m5cqUSExN1++23y93dXUOGDNGiRYts24OCgvTZZ58pISFB3bt3V4sWLTR16lReoQYAAJIuIcDceuutMgzjgtvd3Nw0Y8YMzZgx44JtgoODtWrVqmqP06VLF3355Ze1LQ8AADQCDeYtJAAA0HjU+g4M0KhZM6TC4xfefuxA/dUCAI0YAQaoKWuGlNRTKi2svp2Xv+TfvH5qAoBGigAD1FTh8Yrwcu+bUou2F27n31yyMBI0ANQlAgxQWy3aShHdnF0FADRqdOIFAACmQ4ABAACmwyMkwEkyrUXKLSjRoZx8Z5cCAKZDgAGcINNapJhXtqiotEyS5OfloWYB3k6uCgDMgwADOEFuQYmKSsu0YGg3tQ5pomYB3sotKHF2WQBgGgQYwIlahzTRdZFBkkSAAYBaoBMvAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHQIMAAAwHYcHmLKyMk2ZMkWtWrWSn5+frr32Ws2cOVOGYdjaGIahqVOnKjw8XH5+foqJidHBgwft9nPixAnFxcUpMDBQFotFo0aNUn5+vqPLBQAAJuTwAPPSSy/pjTfe0Guvvab9+/frpZde0ty5c7V48WJbm7lz52rRokVasmSJduzYoYCAAMXGxur06dO2NnFxcdq3b582bNigdevWaevWrYqPj3d0uQAAwIQ8Hb3Dbdu26e6779agQYMkSVdffbXeffdd7dy5U1LF3ZcFCxZo8uTJuvvuuyVJ77zzjkJDQ7V27VoNGzZM+/fv1/r167Vr1y716NFDkrR48WINHDhQ8+bNU0REhKPLBgAAJuLwOzC/+93vtHHjRh04cECS9N133+mrr77SgAEDJEnp6enKyspSTEyM7TtBQUHq1auXkpOTJUnJycmyWCy28CJJMTExcnd3144dOxxdMgAAMBmH34GZOHGi8vLy1L59e3l4eKisrEwvvPCC4uLiJElZWVmSpNDQULvvhYaG2rZlZWUpJCTEvlBPTwUHB9vanKu4uFjFxcW2z3l5eQ47JwAA4Focfgfm/fff18qVK7Vq1Srt3r1bK1as0Lx587RixQpHH8rOnDlzFBQUZFuioqLq9HgAAMB5HB5gxo8fr4kTJ2rYsGHq3Lmzhg8frqefflpz5syRJIWFhUmSsrOz7b6XnZ1t2xYWFqacnBy77WfOnNGJEydsbc41adIknTx50rZkZGQ4+tQAAICLcHiAKSwslLu7/W49PDxUXl4uSWrVqpXCwsK0ceNG2/a8vDzt2LFD0dHRkqTo6GhZrValpKTY2mzatEnl5eXq1atXlcf18fFRYGCg3QIAABomh/eBGTx4sF544QW1bNlSnTp10rfffqv58+fr4YcfliS5ublpzJgxmjVrltq0aaNWrVppypQpioiI0D333CNJ6tChg/r376/Ro0dryZIlKi0tVWJiooYNG8YbSAAAwPEBZvHixZoyZYoef/xx5eTkKCIiQo8++qimTp1qa/Pss8+qoKBA8fHxslqtuvnmm7V+/Xr5+vra2qxcuVKJiYm6/fbb5e7uriFDhmjRokWOLhcAAJiQwwNM06ZNtWDBAi1YsOCCbdzc3DRjxgzNmDHjgm2Cg4O1atUqR5cHmMuxA9Vv928uWeiwDqDxcXiAAeAA/s0lL39pzejq23n5Swk7CTEAGh0CDOCKLFEVwaTw+IXbHDtQEXAKjxNgADQ6BBjAVVmiCCYAcAEOf40aAACgrhFgAACA6RBgAACA6RBgAACA6RBgAACA6RBgAACA6RBgAACA6RBgAACA6RBgAACA6RBgAACA6RBgAACA6RBgAACA6TCZI1DJmnHx2Z8BAC6BAANIFeElqadUWlh9Oy9/yb95/dQEALggAgwgVdx5KS2U7n1TatH2wu38m0uWqPqrCwBQJQIMcLYWbaWIbs6uAgBwEXTiBQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAApkOAAQAAplMnASYzM1N//vOf1bx5c/n5+alz58765ptvbNsNw9DUqVMVHh4uPz8/xcTE6ODBg3b7OHHihOLi4hQYGCiLxaJRo0YpPz+/LsoFAAAm4/AAk5ubq969e8vLy0v//ve/9cMPP+iVV15Rs2bNbG3mzp2rRYsWacmSJdqxY4cCAgIUGxur06dP29rExcVp37592rBhg9atW6etW7cqPj7e0eUCAAAT8nT0Dl966SVFRUVp2bJltnWtWrWy/dkwDC1YsECTJ0/W3XffLUl65513FBoaqrVr12rYsGHav3+/1q9fr127dqlHjx6SpMWLF2vgwIGaN2+eIiIiHF02AAAwEYffgfnoo4/Uo0cP/elPf1JISIiuv/56vfnmm7bt6enpysrKUkxMjG1dUFCQevXqpeTkZElScnKyLBaLLbxIUkxMjNzd3bVjx44qj1tcXKy8vDy7BQAANEwODzCHDx/WG2+8oTZt2ujTTz/VX/7yFz355JNasWKFJCkrK0uSFBoaave90NBQ27asrCyFhITYbff09FRwcLCtzbnmzJmjoKAg2xIVFeXoUwMAAC7C4QGmvLxcN9xwg2bPnq3rr79e8fHxGj16tJYsWeLoQ9mZNGmSTp48aVsyMjLq9HgAAMB5HB5gwsPD1bFjR7t1HTp00NGjRyVJYWFhkqTs7Gy7NtnZ2bZtYWFhysnJsdt+5swZnThxwtbmXD4+PgoMDLRbAABAw+TwANO7d2+lpaXZrTtw4ICuuuoqSRUdesPCwrRx40bb9ry8PO3YsUPR0dGSpOjoaFmtVqWkpNjabNq0SeXl5erVq5ejSwYAACbj8LeQnn76af3ud7/T7Nmzdd9992nnzp1aunSpli5dKklyc3PTmDFjNGvWLLVp00atWrXSlClTFBERoXvuuUdSxR2b/v372x49lZaWKjExUcOGDeMNJAAA4PgAc+ONN+rDDz/UpEmTNGPGDLVq1UoLFixQXFycrc2zzz6rgoICxcfHy2q16uabb9b69evl6+tra7Ny5UolJibq9ttvl7u7u4YMGaJFixY5ulw0FtYMqfD4hbcfO1B/tQAALpvDA4wk3XXXXbrrrrsuuN3NzU0zZszQjBkzLtgmODhYq1atqovy0NhYM6SknlJpYfXtvPwl/+b1UxMA4LLUSYABXErh8Yrwcu+bUou2F27n31yy8Po9AJgBAQaNR4u2UkQ3Z1ehTGuRDuUwrxcAXA4CDFCPMq1Finlli4pKy+Tn5aFmAd7OLgkATIkAA9Sj3IISFZWWacHQbrqxVbAiLX7ntam8O9MswLvK7QAAAgzgFK1DmpwXTpoFeMvPy0NjVqdKkvy8PPT5uL6EGACoAgEGcBGRFj99Pq6vcgtKdCgnX2NWpyq3oIQAAwBVIMAALiTS4kdgAYAacPhUAgAAAHWNAAMAAEyHAAMAAEyHAAMAAEyHAAMAAEyHAAMAAEyHAAMAAEyHcWCAepBpLbINUAcAuHwEGKCOnT2BoyQmcQQAByDAAHXs7AkcW4c0YZJGAHAAAgxQT1qHNNF1kUHOLgMAGgQ68QIAANMhwAAAANMhwAAAANMhwAAAANMhwAAAANMhwAAAANMhwAAAANNhHBjA7I4dqH67f3PJElU/tQBAPSHAAGbl31zy8pfWjK6+nZe/lLCTEAOgQSHAAGZliaoIJoXHL9zm2IGKgFN4nAADoEEhwAAu7FBOfvVzJ1miCCYAGiUCDOBgmdYi5RaUXNakjc0CvOXn5aExq1Pl5+Whz8f1ZQJIADgLAQZwoExrkWJe2aKi0jJb8LgUkRY/fT6ur3aln9CY1anKLSghwADAWXiNGnCg3IISFZWW6YnbWquotEy5BSWXvK9Ii59ahzRxYHUA0HAQYIA6wN0SAKhbBBgAAGA6BBgAAGA6BBgAAGA6BBgAAGA6BBgAAGA6jAMD1KFDOfnOLgEAGqQ6vwPz4osvys3NTWPGjLGtO336tBISEtS8eXM1adJEQ4YMUXZ2tt33jh49qkGDBsnf318hISEaP368zpw5U9flAg5x9ki6laPpNgvwdnZZANBg1OkdmF27dul//ud/1KVLF7v1Tz/9tD7++GN98MEHCgoKUmJiou699159/fXXkqSysjINGjRIYWFh2rZtm3799VeNGDFCXl5emj17dl2WDDhE5Ui6lQPZXc60AgCA89XZHZj8/HzFxcXpzTffVLNmzWzrT548qbfeekvz58/Xbbfdpu7du2vZsmXatm2btm/fLkn67LPP9MMPP+h///d/1a1bNw0YMEAzZ85UUlKSSkoufWRToD5FWvx0XWSQrosMIrwAgIPVWYBJSEjQoEGDFBMTY7c+JSVFpaWlduvbt2+vli1bKjk5WZKUnJyszp07KzQ01NYmNjZWeXl52rdvX5XHKy4uVl5ent0CAAAapjp5hPTee+9p9+7d2rVr13nbsrKy5O3tLYvFYrc+NDRUWVlZtjZnh5fK7ZXbqjJnzhxNnz7dAdUDAABX5/A7MBkZGXrqqae0cuVK+fr6Onr3FzRp0iSdPHnStmRkZNTbsQEAQP1yeIBJSUlRTk6ObrjhBnl6esrT01NbtmzRokWL5OnpqdDQUJWUlMhqtdp9Lzs7W2FhYZKksLCw895Kqvxc2eZcPj4+CgwMtFsAAEDD5PAAc/vtt2vv3r1KTU21LT169FBcXJztz15eXtq4caPtO2lpaTp69Kiio6MlSdHR0dq7d69ycnJsbTZs2KDAwEB17NjR0SUDAACTcXgfmKZNm+q6666zWxcQEKDmzZvb1o8aNUpjx45VcHCwAgMD9cQTTyg6Olo33XSTJOnOO+9Ux44dNXz4cM2dO1dZWVmaPHmyEhIS5OPj4+iSAQCAyThlJN5XX31V7u7uGjJkiIqLixUbG6vXX3/dtt3Dw0Pr1q3TX/7yF0VHRysgIEAjR47UjBkznFEucFGZ1iLlFpQw8i4A1JN6CTCbN2+2++zr66ukpCQlJSVd8DtXXXWVPvnkkzquDA2CNUMqPH7h7ccO1OnhM61Finlli4pKyySJUXcBoB4wFxLMzZohJfWUSgurb+flL/k3r5MScgtKVFRapgVDu6l1SJM6GXW38s4OI/oCQAUCDMyt8HhFeLn3TalF2wu3828uWaLqtJTWIU10XWSQQ/d59pxKUsXdnc/H9SXEAGj0CDBoGFq0lSK6ObsKhzt7TqVDOfkaszpVuQUlBBgAjR4BBnBxkRY/AgsAnKPO5kICGoNMaxFvHgGAE3AHBrhEZ7995PJvHl3sTax66CMEAI5EgAEu0dlvH93YKtg1H/P4N694A2vN6OrbeflLCTsJMQBMgwADXKbWIU1cM7xIFYEkYefFx8lZM7qiDQEGgEkQYICGzhJFMAHQ4NCJFwAAmA4BBgAAmA4BBgAAmA4BBjCZQzn5yrQWObsMAHAqAgxgEmfPixTzyhZCDIBGjbeQ4NqsGRd/BbiRqJwXaVf6CeZEAtDoEWDguqwZUlLPitmmq+PlXzFgWyMQafFTbkgTZ5cBAE5HgIHrKjxeEV7ufbNitukLqedh8DOtRbbZoQEAzkGAgetr0VaK6ObsKiTZz38kyfXnQAKABooAA9TC2fMftQ5pomYB3vRDAQAnIMAAl6B1SBNdFxnk7DIAoNHiNWoAAGA6BBgAAGA6BBgAAGA6BBgAAGA6BBgAAGA6BBighjKtRQxeBwAugteogRo4ewA7Vxu8rnJk4Msek+Zi80rV84jHAFAdAgxQA2cPYHdjq2CXGbzu3GD1+bi+ta/Nv3nFfFJrRlffzstfSthJiAHgEggwQC20DmniMuFF+m+weuK21lq86dClzVBtiaoIJheb9XvN6Io2BBgALoAAA1TDlSduPLumyw5VliiCCQBTIcAAF+CqEzc2C/CWn5eHxqxOleQ6dQFAfSLAABfgqhM3Rlr89Pm4vsotKJFUEWgq/wwAjQUBBrgIV5y4MdLiZxemCDAAGhsCDJzHmnHxjqMAAFSBAAPnsGZIST2l0sLq23n5V7zmCwDAWQgwcI7C4xXh5d43pRZtL9yOwdMAAFUgwMC5WrSVIro5uwoAgMkwFxIAADAdAgwAADAdAgwAADAdhweYOXPm6MYbb1TTpk0VEhKie+65R2lpaXZtTp8+rYSEBDVv3lxNmjTRkCFDlJ2dbdfm6NGjGjRokPz9/RUSEqLx48frzJkzji4XAACYkMMDzJYtW5SQkKDt27drw4YNKi0t1Z133qmCggJbm6efflr/+te/9MEHH2jLli365ZdfdO+999q2l5WVadCgQSopKdG2bdu0YsUKLV++XFOnTnV0uQAAwIQc/hbS+vXr7T4vX75cISEhSklJ0S233KKTJ0/qrbfe0qpVq3TbbbdJkpYtW6YOHTpo+/btuummm/TZZ5/phx9+0Oeff67Q0FB169ZNM2fO1IQJE/T888/L25t5XwAAaMzqvA/MyZMnJUnBwcGSpJSUFJWWliomJsbWpn379mrZsqWSk5MlScnJyercubNCQ0NtbWJjY5WXl6d9+/ZVeZzi4mLl5eXZLUBjcygnX99nnlSmtcjZpQBAnarTcWDKy8s1ZswY9e7dW9ddd50kKSsrS97e3rJYLHZtQ0NDlZWVZWtzdnip3F65rSpz5szR9OnTHXwGgDlUNUP15+P6usTkkwBQF+o0wCQkJOj777/XV199VZeHkSRNmjRJY8eOtX3Oy8tTVBQjuKJxOHuG6kM5+RqzOlW5BSWODzAXm5+KkZMB1JM6CzCJiYlat26dtm7dqiuvvNK2PiwsTCUlJbJarXZ3YbKzsxUWFmZrs3PnTrv9Vb6lVNnmXD4+PvLx8XHwWQDmce4M1Q7l37xiXqo1o6tv5+UvJewkxACocw4PMIZh6IknntCHH36ozZs3q1WrVnbbu3fvLi8vL23cuFFDhgyRJKWlpeno0aOKjo6WJEVHR+uFF15QTk6OQkJCJEkbNmxQYGCgOnbs6OiSAVyMJaoimFxs9vA1oyvaEGAA1DGHB5iEhAStWrVK//znP9W0aVNbn5WgoCD5+fkpKChIo0aN0tixYxUcHKzAwEA98cQTio6O1k033SRJuvPOO9WxY0cNHz5cc+fOVVZWliZPnqyEhATuspiFNePiP3YwF0sUwQSAy3B4gHnjjTckSbfeeqvd+mXLlunBBx+UJL366qtyd3fXkCFDVFxcrNjYWL3++uu2th4eHlq3bp3+8pe/KDo6WgEBARo5cqRmzJjh6HJRF6wZUlLPitmmq+PlX/FoAgCAWqqTR0gX4+vrq6SkJCUlJV2wzVVXXaVPPvnEkaWhvhQerwgv975ZMdv0hbhwh89Ma5EO5eQ7uwwAwAXU6VtIaORatJUiujm7ilrLtBYp5pUtKiotk5+Xh5oFMHAiALgaAgxwjtyCEhWVlmnB0G66sVUwY6kAgAtiNmrgAlqHNDF1eDmUk8+IvAAaLAIM0MCcPSpvzCtbCDEAGiQCDPD/ZVqL9H3mSdN33q0clXfB0G4qKi1TbkGJs0sCAIejDwwg+467kkzfeTfS4qfckCbOLgMA6gwBBpB9x93WIU3ULMDb1P1fAKChI8AAZ2kd0kTXRQY5uwwAwEUQYAA4FjNWA6gHBBgAjsGM1QDqEQEGgGMwYzWAekSAAeA4zFgNoJ4wDgwAADAd7sCg9qwZF39MYCLMPO0EdPQFcJkIMKgda4aU1FMqLay+nZd/xY+Qi2Pm6XpGR18ADkKAQe0UHq8IL/e+KbVoe+F2Jvk/aGaermd09AXgIAQYXJoWbaWIbs6uwmHMPvO0qdDRF4AD0IkXAACYDgEGAACYDo+Q0ChlWouUW1DC20cAYFIEGDQ6Z795JKnBv31UGdKYYRtAQ0KAQaOSaS3SrvQTtjePWoc0abA/7M0CvOXn5aExq1MlVQS1z8f1bTjnerHxiCTTvA0HoPYIMGg0zh3zpaG/Nh1p8dPn4/raHpWNWZ2q3IKShnHOtRmPiPFkgAaJAINGozGO+RJp8TPveVY3Wu+xAxcfj4jxZIAGjQCDRocxX1xcbUbrbRlNOAEaKQIMANdSk9F6Jfq3AI0cAQb2GthEjTApR47Wy8SRQINEgMF/NbCJGtHIMXEk0KARYPBfDWyiRjRyTBwJNGgEGJyvgU3UiP86lJPfYMe9qVJNH0XxmAkwHQIM0AicPahdgxvQ7nLwmAkwLQIMGjzmPfrvoHa70k80rAHtLpdZHzMxCjFAgEHD1tjmPapOpMVPuSFNnF2G63HkG0/1gVGIAUkEGDRwZ4++25DnPUIjUpPO9q541whwMAJMY1GTW84NeIyX1iFNdF1kkLPLgJnVV0ffmo7FRGd7NHIEmMagprecpQY1xkumtahR93uBg9Smo+/Qv0v+LS79WIXHpNXDGYsJqAECTGNQ0/FdJNN2/KvsqFv5iOjcmacba7+XC6kMdjxSq4GadPStDB7/O+Tyj+flL/35/6oPQvV1t8eRxwIcjADTmDTQW87nhpXPx/VtlDNP18TZr1NL4pXqmqpJR9+azN9UE44MDNU99qrN3R46A8MFEWCcyVH/99PI5y+qDCtP3NZaizcd0q70E7ZtzDxtr/J16srXynml2oFc6W2m2jz2qu5uD52B4cJcOsAkJSXp5ZdfVlZWlrp27arFixerZ8+ezi7LMRz1KmQjnr/o3PFdrosMOu/uAo+Ozhdp8SOwNHTM6I1GwGUDzOrVqzV27FgtWbJEvXr10oIFCxQbG6u0tDSFhIQ4u7zL56hXIRvp/EVVje9yXWSQ7e6CRP8ONHL1OaN3TTjibnJN94NGwWUDzPz58zV69Gg99NBDkqQlS5bo448/1ttvv62JEyc6uToHclS/lAbav6VS5d2WSody8i84vguhpXYa3fxIqLmaPoqqiYu9pVWbPjkXe9uLTs6NgksGmJKSEqWkpGjSpEm2de7u7oqJiVFycnKV3ykuLlZxcbHt88mTJyVJeXl5ji/wVLaUn315+zh+SCo2pFP50oVqPJVf0eZIasWfL3U/JvVb3mn9ll+sE4WlGvPetzpdWm633dfLXe2beyqiqZukUuXllTqnUJPyLDst7/LTevKdbfL1cteCYdcr2N/L2WVJkq5o4qMrAn2dXQbcg6ThG6WiExdvW53C49KaeOmte6tv5+kn3fvOhR9312o/Sy/vsXnlsc4U1f2xzKpJqNQ01OG7rfzdNgyj+oaGC8rMzDQkGdu2bbNbP378eKNnz55VfmfatGmGJBYWFhYWFpYGsGRkZFSbFVzyDsylmDRpksaOHWv7XF5erhMnTqh58+Zyc3Nz2HHy8vIUFRWljIwMBQYGOmy/DQXXp3pcn+pxfarH9ake16d6Zrk+hmHo1KlTioiIqLadSwaYFi1ayMPDQ9nZ9o9psrOzFRYWVuV3fHx85OPjY7fOYrHUVYkKDAx06X8Azsb1qR7Xp3pcn+pxfarH9ameGa5PUFDQRdu410Mdtebt7a3u3btr48aNtnXl5eXauHGjoqOjnVgZAABwBS55B0aSxo4dq5EjR6pHjx7q2bOnFixYoIKCAttbSQAAoPFy2QAzdOhQ/fbbb5o6daqysrLUrVs3rV+/XqGhju/xXBs+Pj6aNm3aeY+rUIHrUz2uT/W4PtXj+lSP61O9hnZ93AzjYu8pAQAAuBaX7AMDAABQHQIMAAAwHQIMAAAwHQIMAAAwHQJMFZKSknT11VfL19dXvXr10s6dO6ttb7ValZCQoPDwcPn4+Kht27b65JNP6qna+lfb67NgwQK1a9dOfn5+ioqK0tNPP63Tp0/XU7X1a+vWrRo8eLAiIiLk5uamtWvXXvQ7mzdv1g033CAfHx+1bt1ay5cvr/M6naW212fNmjW64447dMUVVygwMFDR0dH69NNP66fYenYp/3Yqff311/L09FS3bt3qrD5nu5TrU1xcrOeee05XXXWVfHx8dPXVV+vtt9+u+2Kd4FKuz8qVK9W1a1f5+/srPDxcDz/8sI4fv8jklS6EAHOO1atXa+zYsZo2bZp2796trl27KjY2Vjk5OVW2Lykp0R133KEjR47oH//4h9LS0vTmm28qMjKyniuvH7W9PqtWrdLEiRM1bdo07d+/X2+99ZZWr16tv/71r/Vcef0oKChQ165dlZSUVKP26enpGjRokPr166fU1FSNGTNGjzzySIP9ka7t9dm6davuuOMOffLJJ0pJSVG/fv00ePBgffvtt3Vcaf2r7bWpZLVaNWLECN1+++11VJlruJTrc99992njxo166623lJaWpnfffVft2rWrwyqdp7bX5+uvv9aIESM0atQo7du3Tx988IF27typ0aMdMPN4fXHM9IsNR8+ePY2EhATb57KyMiMiIsKYM2dOle3feOMN45prrjFKSkrqq0Snqu31SUhIMG677Ta7dWPHjjV69+5dp3W6AknGhx9+WG2bZ5991ujUqZPduqFDhxqxsbF1WJlrqMn1qUrHjh2N6dOnO74gF1KbazN06FBj8uTJxrRp04yuXbvWaV2uoibX59///rcRFBRkHD9+vH6KciE1uT4vv/yycc0119itW7RokREZGVmHlTkWd2DOUlJSopSUFMXExNjWubu7KyYmRsnJyVV+56OPPlJ0dLQSEhIUGhqq6667TrNnz1ZZWVl9lV1vLuX6/O53v1NKSortMdPhw4f1ySefaODAgfVSs6tLTk62u56SFBsbe8Hr2diVl5fr1KlTCg4OdnYpLmHZsmU6fPiwpk2b5uxSXM5HH32kHj16aO7cuYqMjFTbtm31zDPPqKioyNmluYTo6GhlZGTok08+kWEYys7O1j/+8Q9T/bfZZUfidYZjx46prKzsvNF+Q0ND9Z///KfK7xw+fFibNm1SXFycPvnkEx06dEiPP/64SktLG9x/VC7l+jzwwAM6duyYbr75ZhmGoTNnzuixxx5rsI+QaisrK6vK65mXl6eioiL5+fk5qTLXNG/ePOXn5+u+++5zdilOd/DgQU2cOFFffvmlPD35T/m5Dh8+rK+++kq+vr768MMPdezYMT3++OM6fvy4li1b5uzynK53795auXKlhg4dqtOnT+vMmTMaPHhwrR9hOhN3YC5TeXm5QkJCtHTpUnXv3l1Dhw7Vc889pyVLlji7NJewefNmzZ49W6+//rp2796tNWvW6OOPP9bMmTOdXRpMZtWqVZo+fbref/99hYSEOLscpyorK9MDDzyg6dOnq23bts4uxyWVl5fLzc1NK1euVM+ePTVw4EDNnz9fK1as4C6MpB9++EFPPfWUpk6dqpSUFK1fv15HjhzRY4895uzSaozYfpYWLVrIw8ND2dnZduuzs7MVFhZW5XfCw8Pl5eUlDw8P27oOHTooKytLJSUl8vb2rtOa69OlXJ8pU6Zo+PDheuSRRyRJnTt3VkFBgeLj4/Xcc8/J3b1xZ+iwsLAqr2dgYCB3X87y3nvv6ZFHHtEHH3xw3iO3xujUqVP65ptv9O233yoxMVFSxQ+2YRjy9PTUZ599pttuu83JVTpXeHi4IiMjFRQUZFvXoUMHGYahn3/+WW3atHFidc43Z84c9e7dW+PHj5ckdenSRQEBAerTp49mzZql8PBwJ1d4cY371+Mc3t7e6t69uzZu3GhbV15ero0bNyo6OrrK7/Tu3VuHDh1SeXm5bd2BAwcUHh7eoMKLdGnXp7Cw8LyQUhn2DKbhUnR0tN31lKQNGzZc8Ho2Ru+++64eeughvfvuuxo0aJCzy3EJgYGB2rt3r1JTU23LY489pnbt2ik1NVW9evVydolO17t3b/3yyy/Kz8+3rTtw4IDc3d115ZVXOrEy19Ag/tvszB7Erui9994zfHx8jOXLlxs//PCDER8fb1gsFiMrK8swDMMYPny4MXHiRFv7o0ePGk2bNjUSExONtLQ0Y926dUZISIgxa9YsZ51Cnart9Zk2bZrRtGlT49133zUOHz5sfPbZZ8a1115r3Hfffc46hTp16tQp49tvvzW+/fZbQ5Ixf/5849tvvzV++uknwzAMY+LEicbw4cNt7Q8fPmz4+/sb48ePN/bv328kJSUZHh4exvr16511CnWqttdn5cqVhqenp5GUlGT8+uuvtsVqtTrrFOpMba/NuRr6W0i1vT6nTp0yrrzySuOPf/yjsW/fPmPLli1GmzZtjEceecRZp1Cnant9li1bZnh6ehqvv/668eOPPxpfffWV0aNHD6Nnz57OOoVaI8BUYfHixUbLli0Nb29vo2fPnsb27dtt2/r27WuMHDnSrv22bduMXr16GT4+PsY111xjvPDCC8aZM2fquer6U5vrU1paajz//PPGtddea/j6+hpRUVHG448/buTm5tZ/4fXgiy++MCSdt1Rek5EjRxp9+/Y97zvdunUzvL29jWuuucZYtmxZvdddX2p7ffr27Vtt+4bkUv7tnK2hB5hLuT779+83YmJiDD8/P+PKK680xo4daxQWFtZ/8fXgUq7PokWLjI4dOxp+fn5GeHi4ERcXZ/z888/1X/wlcjMMs9wrAgAAqEAfGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDoEGAAAYDr/D5DP+/jgo2ljAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00226 1.00161 and their SDs are 0.11998 0.03987\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 9129\n",
      "working on HD 300 out of 9129\n",
      "working on HD 600 out of 9129\n",
      "working on HD 900 out of 9129\n",
      "working on HD 1200 out of 9129\n",
      "working on HD 1500 out of 9129\n",
      "working on HD 1800 out of 9129\n",
      "working on HD 2100 out of 9129\n",
      "working on HD 2400 out of 9129\n",
      "working on HD 2700 out of 9129\n",
      "working on HD 3000 out of 9129\n",
      "working on HD 3300 out of 9129\n",
      "working on HD 3600 out of 9129\n",
      "working on HD 3900 out of 9129\n",
      "working on HD 4200 out of 9129\n",
      "working on HD 4500 out of 9129\n",
      "working on HD 4800 out of 9129\n",
      "working on HD 5100 out of 9129\n",
      "working on HD 5400 out of 9129\n",
      "working on HD 5700 out of 9129\n",
      "working on HD 6000 out of 9129\n",
      "working on HD 6300 out of 9129\n",
      "working on HD 6600 out of 9129\n",
      "working on HD 6900 out of 9129\n",
      "working on HD 7200 out of 9129\n",
      "working on HD 7500 out of 9129\n",
      "working on HD 7800 out of 9129\n",
      "working on HD 8100 out of 9129\n",
      "working on HD 8400 out of 9129\n",
      "working on HD 8700 out of 9129\n",
      "working on HD 9000 out of 9129\n",
      "all done checking HD and complement contiguity for all 9129 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    }
   ],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confirming vtd 0 is not in the map but missing from the parent vtd list\n",
      "confirming vtd 2000 is not in the map but missing from the parent vtd list\n",
      "confirming vtd 4000 is not in the map but missing from the parent vtd list\n",
      "confirming vtd 6000 is not in the map but missing from the parent vtd list\n",
      "confirming vtd 8000 is not in the map but missing from the parent vtd list\n"
     ]
    }
   ],
   "source": [
    "for v in range(nVTDs):\n",
    "    if v %2000 == 0:\n",
    "        print(\"confirming vtd\",v,\"is not in the map but missing from the parent vtd list\")\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(\"Uhoh,\",v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "working on full or partial VTD master list for unit 4000\n",
      "working on full or partial VTD master list for unit 6000\n",
      "working on full or partial VTD master list for unit 8000\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9129 9129 9129 9129 9129 9129\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 9129\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append any cut districts and adjust the HDweights\n",
      "52 0 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append any cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth #patchedWithCutsVTDs\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "4b309dad-0e9f-4840-a27f-c571cdcdb2bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working to renormalize unitVTDfracs for all units (including main unit) 5157 associated with tract 5205\n",
      "5157 = unit No, found VTDchild 5205 with fragPop 1286.9947085932715\n",
      "  this unit 5157 has unitPop 1286.9947085932715 and 1 associated VTD fracs\n",
      "    u, VTDfrac,fullVTDlist, partialList 5157 0.673 [] [5205]\n",
      "5158 = unit No, found VTDchild 9139 with fragPop 1109.2789953421009\n",
      "  this unit 5158 has unitPop 1109.2789953421009 and 1 associated VTD fracs\n",
      "    u, VTDfrac,fullVTDlist, partialList 5158 0.58 [] [5205]\n",
      "5159 = unit No, found VTDchild 9140 with fragPop 247.72629606462775\n",
      "  this unit 5159 has unitPop 247.72629606462775 and 1 associated VTD fracs\n",
      "    u, VTDfrac,fullVTDlist, partialList 5159 0.129 [] [5205]\n",
      "renormalizing the sum for units [5157, 5158, 5159] sum was 1.3816254702676913\n",
      "5157 's new fraction of total tract pop is 0.48676047980078346\n",
      "5158 's new fraction of total tract pop is 0.41954576223226203\n",
      "5159 's new fraction of total tract pop is 0.09369375796695453\n"
     ]
    }
   ],
   "source": [
    "#special correction for CA -- did not properly assign frag pops for split tract 5205\n",
    "v = 5205\n",
    "u = allUnits.index(v)\n",
    "print(\"working to renormalize unitVTDfracs for all units (including main unit)\",u,\"associated with tract\",v)\n",
    "listToCorrect, listPartials = list(), list()\n",
    "for i, a in enumerate(allUnits):\n",
    "    if a in VTDchildren[v]:\n",
    "        print(i,\"= unit No, found VTDchild\",a,\"with fragPop\",fragVTDpop[a])\n",
    "        print(\"  this unit\",i,\"has unitPop\",unitPop[i],\"and\",len(unitVTDfrac[i]),\"associated VTD fracs\")\n",
    "        print(\"    u, VTDfrac,fullVTDlist, partialList\",i,r3(unitVTDfrac[i][0]),unitFullVTDlist[i],unitPartialVTDlist[i])\n",
    "        listToCorrect.append(i)\n",
    "sumFrac = np.sum([unitVTDfrac[i] for i in listToCorrect])\n",
    "print(\"renormalizing the sum for units\",listToCorrect,\"sum was\",sumFrac)\n",
    "for u in listToCorrect:\n",
    "    unitVTDfrac[u][0] /= sumFrac\n",
    "    print(u,\"'s new fraction of total tract pop is\",unitVTDfrac[u][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1f43371-c014-4d8e-aeae-7ce3548bd354",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"now, special for CA, rerun the unit --> vtd assignment code\")  #this is an overwrite\n",
    "\n",
    "if STATE == \"CA\":\n",
    "    HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "    print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname  \n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that VTDs are represented 1.0 across all units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"checking that VTDs are represented 1.0 across all units\")\n",
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "plotVTDs, plotUses = list(), list()\n",
    "for v in range(nVTDs):\n",
    "    if tractPop[v] > 0  and v not in allCutVTDs:\n",
    "        plotVTDs.append(v)\n",
    "        plotUses.append(vtdUSE[v])\n",
    "plt.scatter(plotVTDs, plotUses)\n",
    "plt.show()\n",
    "for i,v in enumerate(plotVTDs):\n",
    "    if plotUses[i] > 0.1 and plotUses[i] < 0.9 :\n",
    "        print(v,tractPop[v], plotUses[i],\"vtd no, its pop, and usage across all units\")\n",
    "        plotPoly(tractGeom[v])\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops.  These should all be within single digits\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops.  These should all be within single digits\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()\n",
    "plotThis = False\n",
    "for t in popHDlist:  #seems like for TN, we lost a vtd in converting from unit-based to vtd-based\n",
    "    if abs(HDvPop[t] - HDvtdbasedPop[t]) > 10:\n",
    "        plotThis = True\n",
    "        print(\"t, HDunit-based pop - pop from vtd-based HDs\",t,r3(HDvPop[t] - HDvtdbasedPop[t]) )\n",
    "        plotPoly(tractCP[t].buffer(0.1))\n",
    "if plotThis:\n",
    "    plotPoly(MAP)\n",
    "    print(\"Here are the HD centers with significant pop difference between unit-based and vtd-based HD lists\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for CA\n",
      "In year/election 2020 Dem and GOP total votes were 11104861 6002732 for a stateGOP of 0.35088\n",
      "There are a total of 9129  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 8748378 4481928 for a stateGOP of 0.33876\n",
      "There are a total of 9129  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_t.csv\")  #... _vtd.csv for most states, but CA uses tract-level votes\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for CA\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv CA9129patchedVTDsRedone.csv\n",
      "enter the number of Congr'l districts in the state; e.g. 8 52\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 9129 drawn districts.  This state has 52 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of Congr'l districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = [ str( tractPopFile['GEOID20'][i]) for i in range(len(tractPopFile['GEOID20'])) ] #force strings to match\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "if STATE == \"CA\":  \n",
    "    mergedGEO = [\"0\"+str(mergedDF['GEOID20'][i]) for i in range(len(mergedDF['GEOID20'])) ]  #force strings to match; need leading 0\n",
    "else:\n",
    "    mergedGEO = [str(mergedDF['GEOID20'][i]) for i in range(len(mergedDF['GEOID20'])) ]  #force strings to match\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedGEO, \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "10c34e81-fe49-4859-bbc6-e3e70b0a83b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that our merge worked, esp. for CA\n",
      "06029004402 06029004402\n",
      "9129 total rows after merging on GEOID's vs. expected number 9129\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that our merge worked, esp. for CA\")\n",
    "print(miniHDdf[\"GEOID20\"][0], miniVestDF[\"GEOID20\"][0])\n",
    "print(len(mappingDF),\"total rows after merging on GEOID's vs. expected number\",nVTDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])  #for MA, do GOP-leaning.  Other states, use Dem-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"D\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "working on drawn district no 5000\n",
      "working on drawn district no 6000\n",
      "working on drawn district no 7000\n",
      "working on drawn district no 8000\n",
      "working on drawn district no 9000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 11104861 6002732 -5102128\n",
      "ensemble : 11118448 6015153 -5103294\n",
      "the true and ensemble state GOP leans are 0.35088 0.3510734926286439\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 9129 9129 52\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
